translation of the problem of complete test set generation to pseudo boolean programming an attempt is made to demonstrate that the problem of complete test set generation is amenable to the problem of pseudo boolean programming for this purpose various types of faults viz single faults multiple faults and bridging faults are considered the key issue is to obtain logical expressions for the primary output line in terms of different faulty internal nodes to find real transforms using standard rules the real transforms of a boolean function can be obtained directly without any algebraic manipulation

cellular automata for weighted random pattern generation fault testing random pattern resistant circuits requires that bist built in self test techniques generate large numbers of pseudorandom patterns to shorten these long test lengths this study describes a cellular automata based method that efficiently generates weighted pseudorandom bist patterns this structure called a weighted cellular automaton wca uses no external weighting logic the design algorithm mwcargo combines generation of the necessary weight sets and design of the wca in this study wca pattern generators designed by mwcargo achieved 100 percent coverage of testable stuck at faults for benchmark circuits with random pattern resistant faults the wca applies complete tests much faster than existing test per scan techniques at the same time the hardware overhead of wca proves to be competitive with that of current test per clock schemes

analysing images of curved surfaces a reflectance map makes the relationship between image intensity and surface orientation explicit trade offs between image intensity and surface orientation emerge which cannot be resolved locally in a single view existing methods for determining surface orientation from a single view embody assumptions about surface curvature the hessian matrix is introduced to represent surface curvature properties of surface curvature are expressed as properties of the hessian matrix for several classes of surface image analysis simplifies this result has already been established for planar surfaces forming trihedral corners similar simplification is demonstrated for developable surfaces and for the subclass of surfaces known as generalized cones these studies help to delineate shape information that can be determined from geometric measurements at object boundaries and shape information that can be determined from intensity measurements over sections of smoothly curved surface a novel technique called photometric stereo is discussed the idea of stereo is to obtain multiple images in order to determine the underlying scene precisely in photometric stereo the viewing direction is constant multiple images are obtained by varying the incident illumination it is shown that this provides sufficient information to determine surface orientation at each image point

worst case linear discriminant analysis as scalable semidefinite feasibility problems in this paper we propose an efficient semidefinite programming sdp approach to worst case linear discriminant analysis wlda compared with the traditional lda wlda considers the dimensionality reduction problem from the worst case viewpoint which is in general more robust for classification however the original problem of wlda is non convex and difficult to optimize in this paper we reformulate the optimization problem of wlda into a sequence of semidefinite feasibility problems to efficiently solve the semidefinite feasibility problems we design a new scalable optimization method with a quasi newton method and eigen decomposition being the core components the proposed method is orders of magnitude faster than standard interior point sdp solvers experiments on a variety of classification problems demonstrate that our approach achieves better performance than standard lda our method is also much faster and more scalable than standard interior point sdp solvers based wlda the computational complexity for an sdp with m constraints and matrices of size d by d is roughly reduced from mathcal o m 3 md 3 m 2 d 2 to mathcal o d 3 m d in our case

eigenfaces vs fisherfaces recognition using class specific linear projection we develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression taking a pattern classification approach we consider each pixel in an image as a coordinate in a high dimensional space we take advantage of the observation that the images of a particular face under varying illumination but fixed pose lie in a 3d linear subspace of the high dimensional image space if the face is a lambertian surface without shadowing however since faces are not truly lambertian surfaces and do indeed produce self shadowing images will deviate from this linear subspace rather than explicitly modeling this deviation we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation our projection method is based on fisher s linear discriminant and produces well separated classes in a low dimensional subspace even under severe variation in lighting and facial expressions the eigenface technique another method based on linearly projecting the image space to a low dimensional subspace has similar computational requirements yet extensive experimental results demonstrate that the proposed fisherface method has error rates that are lower than those of the eigenface technique for tests on the harvard and yale face databases

discriminant subspace analysis a fukunaga koontz approach the fisher linear discriminant fld is commonly used in pattern recognition it finds a linear subspace that maximally separates class patterns according to the fisher criterion several methods of computing the fld have been proposed in the literature most of which require the calculation of the so called scatter matrices in this paper we bring a fresh perspective to fld via the fukunaga koontz transform fkt we do this by decomposing the whole data space into four subspaces with different discriminabilities as measured by eigenvalue ratios by connecting the eigenvalue ratio with the generalized eigenvalue we show where the fisher criterion is maximally satisfied we prove the relationship between fld and fkt analytically and propose a unified framework to understanding some existing work furthermore we extend our theory to the multiple discriminant analysis mda this is done by transforming the data into intraclass and extraclass spaces followed by maximizing the bhattacharyya distance based on our fkt analysis we identify the discriminant subspaces of mda fkt and propose an efficient algorithm which works even when the scatter matrices are singular or too large to be formed our method is general and may be applied to different pattern recognition problems we validate our method by experimenting on synthetic and real data

on the covering radius problem for ternary self dual codes in the present paper we develop a method to determine the cost weight distributions and covering radius of extremal ternary self dual codes of various lengths the notion of modified jacobi polynomials is introduced and elementary aspects of it art discussed algebraic foundation has much room for further investigations since the algorithms underlining in our approach to this problem are sound and we can bet many numerical results as far as the computer runs in a reasonable time

on the coveting radius of extremal self dual codes it is known that every self dual binary code which is not doubly even is a child of a doubly even parent it will be shown that an n 2 n 2 2 child of an n n 2 d doubly even parent has covering radius geq d 1 every extremal doubly even 32 16 8 code has covering radius 6 and every extremal doubly even 48 24 12 code has covering radius 8 the complete coset weight distribution of the 32 16 8 quadratic residue code is given as well as bounds or exact values for the covering radii of all extremai doubly even codes of length less than or equal to 96

clutter invariant atr one of the central problems in automated target recognition is to accommodate the infinite variety of clutter in real military environments the principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter such metrics are formulated using second order random field models our results indicate that this approach significantly improves detection classification rates of targets in clutter

jacobi polynomials for singly even self dual codes and the covering radius problems in this correspondence we develop a method to determine the complete coset weight distributions of the class of singly even self dual binary codes our basic tool is the jacobi polynomials for the code it describes and controls the coset weight enumerators as the results of our present method we give the complete coset weight distributions of some extremal singly even self dual codes of lengths 14 22 32 36 and 40 respectively we give the generator matrices of the used codes of lengths 36 and 40 respectively

a nonlinear programming algorithm for solving semidefinite programs via low rank factorization in this paper we present a nonlinear programming algorithm for solving semidefinite programs sdps in standard form the algorithm s distinguishing feature is a change of variables that replaces the symmetric positive semidefinite variable x of the sdp with a rectangular variable r according to the factorization x rr t the rank of the factorization i e the number of columns of r is chosen minimally so as to enhance computational speed while maintaining equivalence with the sdp fundamental results concerning the convergence of the algorithm are derived and encouraging computational results on some large scale test problems are also presented

on covering radii and coset weight distributions of extremal binary self dual codes of length 56 we present a method to determine the complete coset weight distributions of doubly even binary self dual extremal 56 28 12 codes the most important steps are 1 to describe the shape of the basis for the linear space of rigid jacobi polynomials associated with such codes in each index i 2 to describe the basis polynomials for the coset weight enumerators of the assigned coset weight i by means of rigid jacobi polynomials of index i the multiplicity of the cosets of weight i have a connection with the frequency of the rigid reference binary vectors v of weight i for the jacobi polynomials this information is sufficient to determine the complete coset weight distributions determination of the covering radius of the codes is an immediate consequence of this method one important practical advantage of this method is that it is enough to get information on 8190 codewords of weight 12 minimal weight words in each such code for computing every necessary information

convergence behavior of interior point algorithms we show that most interior point algorithms for linear programming generate a solution sequence in which every limit point satisfies the strict complementarity condition these algorithms include all path following algorithms and some potential reduction algorithms the result also holds for the monotone complementarity problem if a strict complementarity solution exists in general the limit point is a solution that maximizes the number of its nonzero components among all solutions

robust regression for face recognition in this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation using a fundamental concept that in general patterns from a single object class lie on a linear subspace barsi and jacobs 2003 1 we develop a linear model representing a probe image as a linear combination of class specific galleries in the presence of noise the well conditioned inverse problem is solved using the robust huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error the proposed robust linear regression classification rlrc algorithm is extensively evaluated for two important cases of robustness i e illumination variations and random pixel corruption illumination invariant face recognition is demonstrated on three standard databases under exemplary evaluation protocols reported in the literature comprehensive comparative analysis with the state of art illumination tolerant approaches indicates a comparable performance index for the proposed rlrc algorithm the efficiency of the proposed approach in the presence of severe random noise is validated under several exemplary noise models such as dead pixel problem salt and pepper noise speckle noise and additive white gaussian noise awgn the rlrc algorithm is found to be favorable compared with the benchmark generative approaches

the complexity of fault detection problems for combinational logic circuits in this correspondence we analyze the computational complexity of fault detection problems for combinational circuits and propose an approach to design for testability although major fault detection problems have been known to be in general np complete they were proven for rather complex circuits in this correspondence we show that these are still np complete even for monotone circuits and thus for unate circuits we show that for k level k 3 monotone unate circuits these problems are still np complete but that these are solvable in polynomial time for 2 level monotone unate circuits a class of circuits for which these fault detection problems are solvable in polynomial time is presented ripple carry adders decoder circuits linear circuits etc belong to this class a design approach is also presented in which an arbitrary given circuit is changed to such an easily testable circuit by inserting a few additional test points

linear regression for face recognition in this paper we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression using a fundamental concept that patterns from a single object class lie on a linear subspace we develop a linear model representing a probe image as a linear combination of class specific galleries the inverse problem is solved using the least squares method and the decision is ruled in favor of the class with the minimum reconstruction error the proposed linear regression classification lrc algorithm falls in the category of nearest subspace classification the algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature a comparative study with state of the art algorithms clearly reflects the efficacy of the proposed approach for the problem of contiguous occlusion we propose a modular lrc approach introducing a novel distance based evidence fusion def algorithm the proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion

robust edge based semidefinite programming relaxation of sensor network localization recently wang zheng boyd and ye siam j optim 19 655 673 2008 proposed a further relaxation of the semidefinite programming sdp relaxation of the sensor network localization problem named edge based sdp esdp in simulation the esdp is solved much faster by interior point method than sdp relaxation and the solutions found are comparable or better in approximation accuracy we study some key properties of the esdp relaxation showing that when distances are exact zero individual trace is not only sufficient but also necessary for a sensor to be correctly positioned by an interior solution we also show via an example that when distances are inexact zero individual trace is insufficient for a sensor to be accurately positioned by an interior solution we then propose a noise aware robust version of esdp relaxation for which small individual trace is necessary and sufficient for a sensor to be accurately positioned by a certain analytic center solution assuming the noise level is sufficiently small for this analytic center solution the position error for each sensor is shown to be in the order of the square root of its trace lastly we propose a log barrier penalty coordinate gradient descent method to find such an analytic center solution in simulation this method is much faster than interior point method for solving esdp and the solutions found are comparable in approximation accuracy moreover the method can distribute its computation over the sensors via local communication making it practical for positioning and tracking in real time

spectral embedded clustering a framework for in sample and out of sample spectral clustering spectral clustering sc methods have been successfully applied to many real world applications the success of these sc methods is largely based on the manifold assumption namely that two nearby data points in the high density region of a low dimensional data manifold have the same cluster label however such an assumption might not always hold on high dimensional data when the data do not exhibit a clear low dimensional manifold structure e g high dimensional and sparse data the clustering performance of sc will be degraded and become even worse than k means clustering in this paper motivated by the observation that the true cluster assignment matrix for high dimensional data can be always embedded in a linear space spanned by the data we propose the spectral embedded clustering sec framework in which a linearity regularization is explicitly added into the objective function of sc methods more importantly the proposed sec framework can naturally deal with out of sample data we also present a new laplacian matrix constructed from a local regression of each pattern and incorporate it into our sec framework to capture both local and global discriminative information for clustering comprehensive experiments on eight real world high dimensional datasets demonstrate the effectiveness and advantages of our sec framework over existing sc methods and k means based clustering methods our sec framework significantly outperforms sc using the nystrom algorithm on unseen data

global registration of multiple point clouds using semidefinite programming consider n points in r d and m local coordinate systems that are related through unknown rigid transforms for each point we are given possibly noisy measurements of its local coordinates in some of the coordinate systems alternatively for each coordinate system we observe the coordinates of a subset of the points the problem of estimating the global coordinates of the n points up to a rigid transform from such measurements comes up in distributed approaches to molecular conformation and sensor network localization and also in computer vision and graphics the least squares formulation of this problem although nonconvex has a well known closed form solution when m 2 based on the singular value decomposition svd however no closed form solution is known for m geq 3 in this paper we demonstrate how the least squares formulation can be relaxed into a convex program namely a semidefinite program sdp by setting up connections between the uniqueness of this sdp and results fr

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

learning spectral clustering with application to speech separation spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in different clusters having low similarity in this paper we derive new cost functions for spectral clustering based on measures of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem minimizing these cost functions with respect to the partition leads to new spectral clustering algorithms minimizing with respect to the similarity matrix leads to algorithms for learning the similarity matrix from fully labelled data sets we apply our learning algorithm to the blind one microphone speech separation problem casting the problem as one of segmentation of the spectrogram

on polynomial time testable combinational circuits the problems of identifying several nontrivial classes of polynomial time testable ptt circuits are shown to be np complete or harder first ptt classes obtained by using circuit decompositions proposed by fujiwara 1988 and chakradhar et al 1990 are considered another type of decompositions based on fanout reconvergent f r pairs which also lead to ptt classes are proposed the problems of obtaining these decompositions and also some structurally similar general graph decompositions are shown to be np complete or harder then the problems of recognizing ptt classes formed by the boolean formulae belonging to the weakly positive weakly negative bijunctive and affine classes are shown to be np complete

an implicit enumeration algorithm to generate tests for combinational logic circuits the d algorithm dalg is shown to be ineffective for the class of combinational logic circuits that is used to implement error correction and translation ecat functions podem path oriented decision making is a new test generation algorithm for combinational logic circuits podem uses an implicit enumeration approach analogous to that used for solving 0 1 integer programming problems it is shown that podem is very efficient for ecat circuits and is significantly more efficient than dalg over the general spectrum of combinational logic circuits a distinctive feature of podem is its simplicity when compared to the d algorithm podem is a complete algorithm in that it will generate a test if one exists heuristics are used to achieve an efficient implicit search of the space of all possible primary input patterns until either a test is found or the space is exhausted

confidence intervals for expected coverage from a beta testability model abstract the testability distribution of a vlsi circuit can be used to predict the fault coverage of a set of test patterns by restricting the standard test pattern generation process to a sample of faults when testability of a vlsi circuit is modeled as a beta distribution the random detection counts obtained have a beta binomial distribution this paper includes a three confidence intervals for the parameters of the beta binomial distribution b a determination of the proper sample size needed such that the theoretical confidence intervals agree with the actual ones and c a determination of the effect of the number of generated tests on confidence interval widths restricting the test generation process to a sample of faults results in major savings in the overall costs of test generation experimental results are given on three of the large combinational benchmark circuits presented at the 1985 international symposium on circuits and systems

compound rank k projections for bilinear analysis in many real world applications data are represented by matrices or high order tensors despite the promising performance the existing 2 d discriminant analysis algorithms employ a single projection model to exploit the discriminant information for projection making the model less flexible in this paper we propose a novel compound rank k projection crp algorithm for bilinear analysis the crp deals with matrices directly without transforming them into vectors and it therefore preserves the correlations within the matrix and decreases the computation complexity different from the existing 2 d discriminant analysis algorithms objective function values of crp increase monotonically in addition the crp utilizes multiple rank k projection models to enable a larger search space in which the optimal solution can be found in this way the discriminant ability is enhanced we have tested our approach on five data sets including uuim cvl pointing 04 usps and coil20 experimental results show that the performance of our proposed crp performs better than other algorithms in terms of classification accuracy

an sdp based divide and conquer algorithm for large scale noisy anchor free graph realization we propose the disco algorithm for graph realization in mathbb r d given sparse and noisy short range intervertex distances as inputs our divide and conquer algorithm works as follows when a group has a sufficiently small number of vertices the basis step is to form a graph realization by solving a semidefinite program the recursive step is to break a large group of vertices into two smaller groups with overlapping vertices these two groups are solved recursively and the subconfigurations are stitched together using the overlapping atoms to form a configuration for the larger group at intermediate stages the configurations are improved by gradient descent refinement the algorithm is applied to the problem of determining protein moleculer structure tests are performed on molecules taken from the protein data bank database for each molecule given 20 30 of the inter atom distances less than 6a that are corrupted by a high level of noise disco is able to reliably and efficiently reconstruct the conformation of large molecules in particular given 30 of distances with 20 multiplicative noise a 13000 atom conformation problem is solved within an hour with a root mean square deviation of 1 6a

relations among some low rank subspace recovery models recovering intrinsic low dimensional subspaces from data distributed on them is a key preprocessing step to many applications in recent years a lot of work has modeled subspace recovery as low rank minimization problems we find that some representative models such as robust principal component analysis r pca robust low rank representation r lrr and robust latent low rank representation r latlrr are actually deeply connected more specifically we discover that once a solution to one of the models is obtained we can obtain the solutions to other models in closed form formulations since r pca is the simplest our discovery makes it the center of low rank subspace recovery models our work has two important implications first r pca has a solid theoretical foundation under certain conditions we could find globally optimal solutions to these low rank models at an overwhelming probability although these models are nonconvex second we can obtain significantly faster algorithms for these models by solving r pca first the computation cost can be further cut by applying low complexity randomized algorithms for example our novel filtering algorithm to r pca although for the moment the formal proof of our filtering algorithm is not yet available experiments verify the advantages of our algorithm over other state of the art methods based on the alternating direction method

global continuation for distance geometry problems distance geometry problems arise in the determination of protein structure we consider the case where only a subset of the distances between atoms is given and formulate this distance geometry problem as a global minimization problem with special structure we show that global smoothing techniques and a continuation approach for global optimization can be used to determine global solutions of this problem reliably and efficiently the global continuation approach determines a global solution with less computational effort than is required by a standard multistart algorithm moreover the continuation approach usually finds the global solution from any given starting point while the multistart algorithm tends to fail

a distributed sdp approach for large scale noisy anchor free graph realization with applications to molecular conformation we propose a distributed algorithm for solving euclidean metric realization problems arising from large 3 d graphs using only noisy distance information and without any prior knowledge of the positions of any of the vertices in our distributed algorithm the graph is first subdivided into smaller subgraphs using intelligent clustering methods then a semidefinite programming relaxation and gradient search method are used to localize each subgraph finally a stitching algorithm is used to find affine maps between adjacent clusters and the positions of all points in a global coordinate system are then derived in particular we apply our method to the problem of finding the 3 d molecular configurations of proteins based on a limited number of given pairwise distances between atoms the protein molecules all with known molecular configurations are taken from the protein data bank our algorithm is able to reconstruct reliably and efficiently the configurations of large protein molecules from a limited number of pairwise distances corrupted by noise without incorporating domain knowledge such as the minimum separation distance constraints derived from van der waals interactions

explicit sensor network localization using semidefinite representations and facial reductions the sensor network localization snl problem in embedding dimension r consists of locating the positions of wireless sensors given only the distances between sensors that are within radio range and the positions of a subset of the sensors called anchors current solution techniques relax this problem to a weighted nearest positive semidefinite programming sdp completion problem by using the linear mapping between euclidean distance matrices edm and semidefinite matrices the resulting sdp is solved using primal dual interior point solvers yielding an expensive and inexact solution this relaxation is highly degenerate in the sense that the feasible set is restricted to a low dimensional face of the sdp cone implying that the slater constraint qualification fails cliques in the graph of the snl problem give rise to this degeneracy in the sdp relaxation in this paper we take advantage of the absence of the slater constraint qualification and derive a technique for the snl problem with exact data that explicitly solves the corresponding rank restricted sdp problem no sdp solvers are used for randomly generated instances we are able to efficiently solve many huge instances of this np hard problem to high accuracy by finding a representation of the minimal face of the sdp cone that contains the sdp matrix representation of the edm the main work of our algorithm consists in repeatedly finding the intersection of subspaces that represent the faces of the sdp cone that correspond to cliques of the snl problem

fast computation of low rank matrix approximations given a matrix a it is often desirable to find a good approximation to a that has low rank we introduce a simple technique for accelerating the computation of such approximations when a has strong spectral features that is when the singular values of interest are significantly greater than those of a random matrix with size and entries similar to a our technique amounts to independently sampling and or quantizing the entries of a thus speeding up computation by reducing the number of nonzero entries and or the length of their representation our analysis is based on observing that the acts of sampling and quantization can be viewed as adding a random matrix n to a whose entries are independent random variables with zero mean and bounded variance since with high probability n has very weak spectral features we can prove that the effect of sampling and quantization nearly vanishes when a low rank approximation to a p n is computed we give high probability bounds on the quality of our approximation both in the frobenius and the 2 norm

a new discriminant subspace analysis approach for multi class problems fukunaga koontz transform fkt is a famous feature extraction method in statistical pattern recognition which aims to find a set of vectors that have the best representative power for one class while the poorest representative power for the other class li and savvides 1 propose a one against all strategy to deal with multi class problems in which the two class fkt method can be directly applied to find the presentative vectors of each class motivated by the fkt method in this paper we propose a new discriminant subspace analysis dsa method for the multi class feature extraction problems to solve dsa we propose an iterative algorithm for the joint diagonalization jd problem finally we generalize the linear dsa method to handle nonlinear feature extraction problems via the kernel trick to demonstrate the effectiveness of the proposed method for pattern recognition problems we conduct extensive experiments on real data sets and show that the proposed method outperforms most commonly used feature extraction methods

recovery guarantees for exemplar based clustering for a certain class of distributions we prove that the linear programming relaxation of k medoids clustering a variant of k means clustering where means are replaced by exemplars from within the dataset distinguishes points drawn from nonoverlapping balls with high probability once the number of points drawn and the separation distance between any two balls are sufficiently large our results hold in the nontrivial regime where the separation distance is small enough that points drawn from different balls may be closer to each other than points drawn from the same ball in this case clustering by thresholding pairwise distances between points can fail we also exhibit numerical evidence of high probability recovery in a substantially more permissive regime

atpg for heat dissipation minimization during test application a automatic test pattern generator atpg algorithm is proposed that reduces switching activity between successive test vectors during test application the main objective is to permit safe and inexpensive testing of low power circuits and bare die that might otherwise require expensive heat removal equipment for testing at high speeds three new cost functions namely transition controllability observability and test generation costs have been defined it has been shown for a fanout free circuit under test that the transition test generation cost for a fault is the minimum number of transitions required to test a given stuck at fault the proposed algorithm has been implemented and the generated tests are compared with those generated by a standard podem implementation for the larger iscas85 benchmark circuits the results clearly demonstrate that the tests generated using the proposed atpg can decrease the average number of weighted transitions between successive test vectors by a factor of 2 to 23

architecture of a testable analog fuzzy logic controller the authors discuss problems of testability of analog fuzzy logic controllers implemented as vlsi chips enhancements to standard architecture of fuzzy logic controllers which facilitate testing are proposed to improve controllability and observability of internal nodes analog switching blocks are introduced together with some additional circuitry these blocks allow one to test each basic cell of a fuzzy logic controller e g membership function cell minimax cell etc separately the analog switching blocks do not contribute to the power consumption in a working chip end therefore can be used in low power analog fuzzy logic controllers

semantic preserving distance metric learning and applications how do we accurately browse a large set of images or efficiently annotate the images from an image library image clustering methods are invaluable tools for applications such as content based image retrieval and image annotation to perform these tasks it is critical to have proper features to describe the visual and semantic content of images and to define an accurate distance metric to measure the dissimilarity between any two images however existing methods which adopt the features of color histograms edge direction histograms and shape context lack the ability to describe semantic content to solve this problem we propose a new approach that utilizes user provided pairwise constraints to describe the semantic relationship between two images a semantic preserving distance metric learning sp dml algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space in this space the learned distance metric can be used to measure the dissimilarity between two images specifically the manifold structure adopted in sp dml is revealed by the image s visual features to integrate semantic contents in distance metric learning sp dml utilizes pairwise constraints to build semantic patches and align these patches to obtain the optimal distance metric for the new feature space experimental results in image clustering demonstrate that the performance of sp dml is appealing

on the acceleration of test generation algorithms in order to accelerate an algorithm for test generation it is necessary to reduce the number of backtracks in the algorithm and to shorten the process time between backtracks in this paper we consider several techniques to accelerate test generation and present a new test generation algorithm called fan fan out oriented test generation algorithm it is shown that the fan algorithm is faster and more efficient than the podem algorithm reported by goel we also present an automatic test generation system composed of the fan algorithm and the concurrent fault simulation experimental results on large combinational circuits of up to 3000 gates demonstrate that the system performs test generation very fast and effectively

test generation for iterative logic arrays based on an n cube of cell states model the authors present a novel approach to the test generation problem for a more general class of two dimensional iterative logic arrays ilas than considered by previous researchers for certain ilas it is possible to find a test set whose size remains fixed irrespective of the size of the ila while for others it varies with array size given an arbitrary ila cell truth table and a cell interconnection structure the goal is to determine if a fixed size test can be found if not then a test set whose size grows as slowly as possible with the size of the array should be found the authors propose a new model called the n cube of cell states model for representing the cell truth table and interconnection structure the test generation problem is shown to be related to certain properties of cycles in a set of graphs obtained from this model by careful analysis of these cycles efficient testing schedules can be obtained the proposed technique can be applied to unilateral as well as regular bilateral ilas in which the bilateral direction of signal flow are restricted to lie along the horizontal axis

towards robust discriminative projections learning via non greedy l2 1 norm minmax linear discriminant analysis lda is one of the most successful supervised dimensionality reduction methods and has been widely used in many real world applications however the l 2 norm is employed as the distance measure in objective of lda which is sensitive to outliers many previous works improve the robustness of lda by using l1 norm distance however the robustness against outliers is limited and the solver of l1 norm are mostly based on greedy search strategy which is time consuming and easy to get stuck in a local optimum in this paper we propose a novel robust lda measured by l 2 1 norm to learn robust discriminative projections since the proposed model needs to minimize and maximize minmax l 2 1 norm terms simultaneously it is challenging to solve as a result we first systematically derive an efficient iterative optimization algorithm to solve a general ratio minimization problem and rigorously prove its convergence more importantly an efficient non greedy iterative re weighted optimization algorithm is developed based on preceding approach for solving proposed l 2 1 norm minmax problem besides an optimal weighted mean mechanism is driven according to designed objective and solver which can be applied to other approach for robustness improvement extensive experimental results on several real world datasets show the effectiveness of proposed method

polynomial time testability of circuits generated by input decomposition considers polynomial time testability of combinational circuits generated by input decomposition especially those generated by the logic synthesis tool factor first the complexity of the fault detection problem in this class of circuits is explored using a stuck at fault model an o 2 sup k m algorithm for detecting a single stuck at fault is given that is faster than the o 16 sup k m previously reported best algorithm proposed by fujiwara 1990 where k is the number of inputs in a subcircuit and m the number of signal lines in the circuit efficient polynomial time algorithms are described for generating a test set for all single stuck at faults in the circuit the basic strategy is to eliminate backtracks during line justification by constructing tables or vector sets in each subcircuit which makes the fault propagation procedure very simple and eventually results in an efficient test generation procedure this presentation of efficient polynomial time test generation algorithms for factor generated circuits is important since it shows that it is possible to synthesize circuits that are optimized for area and are polynomial time testable at the same time

image clustering using local discriminant models and global integration in this paper we propose a new image clustering algorithm referred to as clustering using local discriminant models and global integration ldmgi to deal with the data points sampled from a nonlinear manifold for each data point we construct a local clique comprising this data point and its neighboring data points inspired by the fisher criterion we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique to obtain the clustering result we further propose a unified objective function to globally integrate the local models of all the local cliques with the unified objective function spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples we show that ldmgi shares a similar objective function with the spectral clustering sc algorithms e g normalized cut ncut in contrast to ncut in which the laplacian matrix is directly calculated based upon a gaussian function a new laplacian matrix is learnt in ldmgi by exploiting both manifold structure and local discriminant information we also prove that k means and discriminative k means diskmeans are both special cases of ldmgi extensive experiments on several benchmark image datasets demonstrate the effectiveness of ldmgi we observe in the experiments that ldmgi is more robust to algorithmic parameter when compared with ncut thus ldmgi is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters

euclidean distance geometry and applications euclidean distance geometry is the study of euclidean geometry based on the concept of distance this is useful in several applications where the input data consist of an incomplete set of distances and the output is a set of points in euclidean space realizing those given distances we survey the theory of euclidean distance geometry and its most important applications with special emphasis on molecular conformation problems

convex sparse spectral clustering single view to multi view spectral clustering sc is one of the most widely used methods for data clustering it first finds a low dimensional embedding u of data by computing the eigenvectors of the normalized laplacian matrix and then performs k means on text u top to get the final clustering result in this paper we observe that in the ideal case text u text u top should be block diagonal and thus sparse therefore we propose the sparse sc ssc method that extends the sc with sparse regularization on text u text u top to address the computational issue of the nonconvex ssc model we propose a novel convex relaxation of ssc based on the convex hull of the fixed rank projection matrices then the convex ssc model can be efficiently solved by the alternating direction method of multipliers furthermore we propose the pairwise ssc that extends ssc to boost the clustering performance by using the multi view information of data experimental comparisons with several baselines on real world datasets testify to the efficacy of our proposed methods

detection and segmentation of generic shapes based on affine modeling of energy in eigenspace this paper presents a novel approach for detection and segmentation of man made generic shapes in cluttered images the set of shapes to be detected are members of affine transformed versions of basic geometric shapes such as rectangles circles etc the shape set is represented by its vectorial edge map transformed over a wide range of affine parameters we use vectorial boundary instead of regular boundary to improve the robustness to noise background clutter and partial occlusion our approach consists of a detection stage and a verification stage in the detection stage we first derive the energy from the principal eigenvectors of the set next an a posteriori probability map of energy distribution is computed from the projection of the edge map representation in a vectorial eigen space local peaks of the posterior probability map are located and indicate candidate detections we use energy probability based detection since we find that the underlying distribution is not gaussian and resembles a hypertoroid in the verification stage each candidate is verified using a fast search algorithm based on a novel representation in angle space and the corresponding pose information of the detected shape is obtained the angular representation used in the verification stage yields better results than a euclidean distance representation experiments are performed in various interfering distortions and robust detection and segmentation are achieved

recognizing imprecisely localized partially occluded and expression variant faces from a single sample per class the classical way of attempting to solve the face or object recognition problem is by using large and representative data sets in many applications though only one sample per class is available to the system in this contribution we describe a probabilistic approach that is able to compensate for imprecisely localized partially occluded and expression variant faces even when only one single training sample per class is available to the system to solve the localization problem we find the subspace within the feature space e g eigenspace that represents this error for each of the training images to resolve the occlusion problem each face is divided into k local regions which are analyzed in isolation in contrast with other approaches where a simple voting space is used we present a probabilistic method that analyzes how good a local match is to make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image

a least squares framework for component analysis over the last century component analysis ca methods such as principal component analysis pca linear discriminant analysis lda canonical correlation analysis cca locality preserving projections lpp and spectral clustering sc have been extensively used as a feature extraction step for modeling classification visualization and clustering ca techniques are appealing because many can be formulated as eigen problems offering great potential for learning linear and nonlinear representations of data in closed form however the eigen formulation often conceals important analytic and computational drawbacks of ca techniques such as solving generalized eigen problems with rank deficient matrices e g small sample size problem lacking intuitive interpretation of normalization factors and understanding commonalities and differences between ca methods this paper proposes a unified least squares framework to formulate many ca methods we show how pca lda cca lpp sc and its kernel and regularized extensions correspond to a particular instance of least squares weighted kernel reduced rank regression ls wkrrr the ls wkrrr formulation of ca methods has several benefits 1 provides a clean connection between many ca techniques and an intuitive framework to understand normalization factors 2 yields efficient numerical schemes to solve ca techniques 3 overcomes the small sample size problem 4 provides a framework to easily extend ca methods we derive weighted generalizations of pca lda sc and cca and several new ca techniques

solving large scale sparse semidefinite programs for combinatorial optimization we present a dual scaling interior point algorithm and show how it exploits the structure and sparsity of some large scale problems we solve the positive semidefinite relaxation of combinatorial and quadratic optimization problems subject to boolean constraints we report the first computational results of interior point algorithms for approximating maximum cut semidefinite programs with dimension up to 3 000

face recognition using elasticfaces though principle component analysis pca and locality preserving projections lpps are two of the most popular linear methods for face recognition pca can only see the euclidean structure of the training set and lpp preserves the nonlinear submanifold structure hidden in the training set in this paper we propose the elastic preserving projections epps which by incorporating the merits of the local geometry and the global information of the training set epp outputs a sample subspace which simultaneously preserves the local geometrical structure and exploits the global information of the training set different from some other linear dimensionality reduction methods epp can be deemed as learning both the coordinates and the affinities between sample points furthermore the effectiveness of our proposed algorithm is analyzed theoretically and confirmed by some experiments on several well known face databases the obtained results indicate that epp significantly outperforms its other rival algorithms

efficient semidefinite spectral clustering via lagrange duality we propose an efficient approach to semidefinite spectral clustering ssc which addresses the frobenius normalization with the positive semidefinite p s d constraint for spectral clustering compared with the original frobenius norm approximation based algorithm the proposed algorithm can more accurately find the closest doubly stochastic approximation to the affinity matrix by considering the p s d constraint in this paper ssc is formulated as a semidefinite programming sdp problem in order to solve the high computational complexity of sdp we present a dual algorithm based on the lagrange dual formalization two versions of the proposed algorithm are proffered one with less memory usage and the other with faster convergence rate the proposed algorithm has much lower time complexity than that of the standard interior point based sdp solvers experimental results on both the uci data sets and real world image data sets demonstrate that 1 compared with the state of the art spectral clustering methods the proposed algorithm achieves better clustering performance and 2 our algorithm is much more efficient and can solve larger scale ssc problems than those standard interior point sdp solvers

constraint satisfaction using constraint logic programming abstract constraint logic programming clp is a new class of declarative programming languages whose primitive operations are based on constraints e g constraint solving and constraint entailment clp languages naturally combine constraint propagation with nondeterministic choices as a consequence they are particularly appropriate for solving a variety of combinatorial search problems using the global search paradigm with short development time and efficiency comparable to procedural tools based on the same approach in this paper we describe how the clp language cc fd a successor of chip using consistency techniques over finite domains can be used to solve two practical applications test pattern generation and car sequencing for both applications we present the cc fd program describe how constraint solving is performed report experimental results and compare the approach with existing tools

on the low rank solutions for linear matrix inequalities in this paper we present a polynomial time procedure to find a low rank solution for a system of linear matrix inequalities lmi the existence of such a low rank solution was shown in the work of au yeung and poon and the work of barvinok in the approach of au yeung and poon an earlier unpublished manuscript of bohnenblust played an essential role both proofs in the work of au yeung and poon and that of barvinok are nonconstructive in nature the aim of this paper is to provide a polynomial time constructive procedure to find such a low rank solution approximatively extensions of our new results and their relations to some of the known results in the literature are discussed

solving large scale semidefinite programs via an iterative solver on the augmented systems the search directions in an interior point method for large scale semidefinite programming sdp can be computed by applying a krylov iterative method to either the schur complement equation sce or the augmented equation both methods suffer from slow convergence as interior point iterates approach optimality numerical experiments have shown that a diagonally preconditioned conjugate residual method on the sce typically takes a huge number of steps to converge however it is difficult to incorporate cheap and effective preconditioners into the sce this paper proposes to apply the preconditioned symmetric quasi minimal residual psqmr method to a reduced augmented equation that is derived from the augmented equation by utilizing the eigenvalue structure of the interior point iterates numerical experiments on sdp problems arising from maximum clique and selected sdplib sdp library problems show that moderately accurate solutions can be obtained with a modest number of psqmr steps using the proposed preconditioned reduced augmented equation an sdp problem with 127600 constraints is solved in about 6 5 hours to an accuracy of 10 6 in relative duality gap

on covering radii and coset weight distributions of extremal binary self dual codes of length 40 in the present paper we develop a method to determine the coset weight distributions and covering radius of doubly even self dual extremal binary codes of length 40 the method is algebraic in nature and largely eliminates necessary computations by electronic computers the method easily applies to longer codes e g self dual 56 28 12 binary codes or to non extremal codes

solving semidefinite quadratic linear programs using sdpt3 this paper discusses computational experiments with linear optimization problems involving semidefinite quadratic and linear cone constraints sqlps many test problems of this type are solved using a new release of sdpt3 a matlab implementation of infeasible primal dual path following algorithms the software developed by the authors uses mehrotra type predictor corrector variants of interior point methods and two types of search directions the hkm and nt directions a discussion of implementation details is provided and computational results on problems from the sdplib and dimacs challenge collections are reported

approximate orthogonal sparse embedding for dimensionality reduction locally linear embedding lle is one of the most well known manifold learning methods as the representative linear extension of lle orthogonal neighborhood preserving projection onpp has attracted widespread attention in the field of dimensionality reduction in this paper a unified sparse learning framework is proposed by introducing the sparsity or l 1 norm learning which further extends the lle based methods to sparse cases theoretical connections between the onpp and the proposed sparse linear embedding are discovered the optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition we also show that the proposed model can be viewed as a general model for sparse linear and nonlinear kernel subspace learning based on this general model sparse kernel embedding is also proposed for nonlinear sparse feature extraction extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm particularly in the cases of small sample sizes

gender classification from unaligned facial images using support subspaces rough face alignments result in suboptimal performance of face identification in this study we present an approach for identifying the gender based on facial images without proper face alignments instead of just using only the detected face patch for identification a set of patches is randomly cropped around the face detection region each patch set is represented by a linear subspace and compared with other linear subspaces by measuring their canonical correlations a similarity matrix comprised of the canonical correlations is then incorporated into an indefinite kernel support vector machine svm formulation the number of support vectors which we call support subspaces can be decided automatically hence we can avoid the dimension selection problem observed in our previous work our experimental results demonstrate that the proposed approach outperforms state of the art methods

on affine motions and bar frameworks in general position abstract a configuration p in r dimensional euclidean space is a finite collection of points p 1 p n that affinely span r r a bar framework denoted by g p in r r is a simple graph g on n vertices together with a configuration p in r r a given bar framework g p is said to be universally rigid if there does not exist another configuration q in any euclidean space not obtained from p by a rigid motion such that q i q j p i p j for each edge i j of g it is known 2 7 that if configuration p is generic and bar framework g p in r r admits a positive semidefinite stress matrix s of rank n r 1 then g p is universally rigid connelly asked 9 whether the same result holds true if the genericity assumption of p is replaced by the weaker assumption of general position we answer this question in the affirmative in this paper

efficient and robust feature extraction by maximum margin criterion in pattern recognition feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information principal component analysis pca and linear discriminant analysis lda are the two most popular linear dimensionality reduction methods however pca is not very effective for the extraction of the most discriminant features and lda is not stable due to the small sample size problem in this paper we propose some new linear and nonlinear feature extractors based on maximum margin criterion mmc geometrically feature extractors based on mmc maximize the average margin between classes after dimensionality reduction it is shown that mmc can represent class separability better than pca as a connection to lda we may also derive lda from mmc by incorporating some constraints by using some other constraints we establish a new linear feature extractor that does not suffer from the small sample size problem which is known to cause serious stability problems for lda the kernelized nonlinear counterpart of this linear feature extractor is also established in the paper our extensive experiments demonstrate that the new feature extractors are effective stable and efficient

the complexity of very simple boolean formulas with applications the concepts of textbf sat hardness and textbf sat completeness modulo npolylogn time and linear size reducibility denoted by textbf sat hard npolylogn n and textbf sat complete npolylogn n respectively are introduced regardless of whether textbf p textbf np or textbf p neq textbf np it is shown that intuitivelyeach textbf sat hard npolylogn n problem requires essentially at least as much deterministic time as andeach textbf sat complete npolylogn n problem requires essentially the same deterministic time as the satisfiability problem for 3cnf formulas it is proved that the leqq satisfiability tautology unique satisfiability equivalence and minimization problems are already textbf sat complete npolylogn n for very simple boolean formulas and for very simple systems of boolean equations these completeness results are used to characterize the deterministic time complexities of a number of problems for lattices propositional calculi

static test compaction for full scan circuits based on combinational test sets and nonscan input sequences and a lower bound on the number of tests a new class of static compaction procedures is described that generate test sets with reduced test application times for scan circuits the proposed class of procedures combines the advantages of two earlier static compaction procedures one that tends to generate large numbers of tests with a short primary input sequence included in every test and one that tends to generate small numbers of tests with a long primary input sequence included in one of the tests a procedure of the proposed class starts from an initial test set that has a large number of tests and long primary input sequences and it selects a subset of the tests and subsequences of their primary input sequences it thus has the flexibility of finding an appropriate balance between the number of tests and the lengths of the primary input sequences in order to minimize the test application time several ways of computing the primary input sequences for the initial test set are considered the most compact test sets are obtained when a test sequence for the nonscan circuit is available and this sequence is used as part of every test in the initial test set however it is shown that high levels of compaction can also be achieved without the overhead of test generation for the nonscan circuit specifically we show that the industry practice of holding a primary input vector constant between scan operations can be accommodated we estimate the ability of the procedure to achieve optimum test sets by computing a lower bound on the number of tests and demonstrating that the procedure achieves or approaches this lower bound

on stress matrices of d 1 lateration frameworks in general position let g p be a bar framework of n vertices in general position in mathbb r d for d n 1 where g is a d 1 lateration graph in this paper we present a constructive proof that g p admits a positive semidefinite stress matrix with rank n d 1 we also prove a similar result for a sensor network where the graph consists of m d 1 anchors

distributed semidefinite programming with application to large scale system analysis distributed algorithms for solving coupled semidefinite programs commonly require many iterations to converge they also put high computational demand on the computational agents in this paper we show that in case the coupled problem has an inherent tree structure it is possible to devise an efficient distributed algorithm for solving such problems the proposed algorithm relies on predictor corrector primal dual interior point methods where we use a message passing algorithm to compute the search directions distributedly message passing here is closely related to dynamic programming over trees this allows us to compute the exact search directions in a finite number of steps this is because computing the search directions requires a recursion over the tree structure and hence terminates after an upward and downward pass through the tree furthermore this number can be computed a priori and only depends on the coupling structure of the problem we use the proposed algorithm for analyzing robustness of large scale uncertain systems distributedly we test the performance of this algorithm using numerical examples

efficient parallel search algorithm for determining optimal r 1 2 systematic convolutional self doubly orthogonal codes a novel parallel and implicitly exhaustive search algorithm for finding in systematic form rate r frac 1 2 optimal span convolutional self doubly orthogonal cdo codes and simplified convolutional self doubly orthogonal s cdo codes is presented in order to obtain high performance low latency codecs with these codes it is important to minimize their constraint length or span for a given j number of generator connections the proposed exhaustive algorithm uses algorithmic enhancements over the best previously published searching techniques yielding new and improved codes we were able to obtain new optimal span cdo s cdo codes having order j 9 and j 10 11 respectively as well as new codes having the shortest spans published to date for higher values of j j 10 12 17 and j 12 20 for cdo and s cdo codes respectively the new codes and their error performance are provided an analysis of the evolution of the cdo s cdo code error performance as j increases is presented and the shortest cdo s cdo code span values for each given j are compared

universal rigidity and edge sparsification for sensor network localization owing to their high accuracy and ease of formulation there has been great interest in applying convex optimization techniques particularly that of semidefinite programming sdp relaxation to tackle the sensor network localization problem in recent years however a drawback of such techniques is that the resulting convex program is often expensive to solve in order to speed up computation various edge sparsification heuristics have been proposed whose aim is to reduce the number of edges in the input graph although these heuristics do reduce the size of the convex program and hence make it faster to solve they are often ad hoc in nature and do not preserve the localization properties of the input as such one often has to face a tradeoff between solution accuracy and computational effort in this paper we propose a novel edge sparsification heuristic that can provably preserve the localization properties of the original input at the heart of our heuristic is a graph decomposition procedure which allows us to identify certain sparse generically universally rigid subgraphs of the input graph our computational results show that the proposed approach can significantly reduce the computational and memory complexities of sdp based algorithms for solving the sensor network localization problem moreover it compares favorably with existing speedup approaches both in terms of accuracy and solution time

spaseloc an adaptive subproblem algorithm for scalable wireless sensor network localization an adaptive rule based algorithm spaseloc is described to solve localization problems for ad hoc wireless sensor networks a large problem is solved as a sequence of very small subproblems each of which is solved by semidefinite programming relaxation of a geometric optimization model the subproblems are generated according to a set of sensor anchor selection rules computational results compared with existing approaches show that the spaseloc algorithm scales well and provides excellent localization accuracy

kernel self optimization learning for kernel based feature extraction and recognition kernel learning is becoming an important research topic in the area of machine learning and it has wide applications in pattern recognition computer vision image and signal processing kernel learning provides a promising solution to nonlinear problems including nonlinear feature extraction classification and clustering however in kernel based systems the problem of the kernel function and its parameters remains to be solved methods of choosing parameters from a discrete set of values have been presented in previous studies but these methods do not change the data distribution structure in the kernel based mapping space accordingly performance is not improved because the current kernel optimization does not change the data distribution based on this problem this paper presents a uniform framework for kernel self optimization with the ability to adjust the data structure the data dependent kernel is extended and applied to kernel learning and optimization equations with two criteria for measuring data discrimination are used to solve the optimal parameter values some experiments are performed to evaluate the performance in popular kernel learning methods including kernel principal components analysis kpca kernel discriminant analysis kda and kernel locality preserving projection klpp these evaluations show that the framework of kernel self optimization is feasible for enhancing kernel based learning methods

gender discriminating models from facial surface normals in this paper we show how to use facial shape information to construct discriminating models for gender classification we represent facial shapes using 2 5d fields of facial surface normals and investigate three different methods to improve the gender discriminating capacity of the model constructed using the standard eigenspace method the three methods are novel variants of principal geodesic analysis pga namely a weighted pga b supervised weighted pga and c supervised pga our starting point is to define a weight map over the facial surface that indicates the importance of different locations in discriminating gender we show how to compute the relevant weights and how to incorporate the weights into the 2 5d model construction we evaluate the performance of the alternative methods using facial surface normals extracted from 3d range images or recovered from brightness images experimental results demonstrate the effectiveness of our methods moreover the classification accuracy which is as high as 97 demonstrates the effectiveness of using facial shape information for gender classification

face recognition using an enhanced independent component analysis approach this paper is concerned with an enhanced independent component analysis ica and its application to face recognition typically face representations obtained by ica involve unsupervised learning and high order statistics in this paper we develop an enhancement of the generic ica by augmenting this method by the fisher linear discriminant analysis lda hence its abbreviation fica the fica is systematically developed and presented along with its underlying architecture a comparative analysis explores four distance metrics as well as classification with support vector machines svms we demonstrate that the fica approach leads to the formation of well separated classes in low dimension subspace and is endowed with a great deal of insensitivity to large variation in illumination and facial expression the comprehensive experiments are completed for the facial recognition technology feret face database a comparative analysis demonstrates that fica comes with improved classification rates when compared with some other conventional approaches such as eigenface fisherface and the ica itself

face recognition using ipca ica algorithm in this paper a fast incremental principal non gaussian directions analysis algorithm called ipca ica is introduced this algorithm computes the principal components of a sequence of image vectors incrementally without estimating the covariance matrix so covariance free and at the same time transforming these principal components to the independent directions that maximize the non gaussianity of the source two major techniques are used sequentially in a real time fashion in order to obtain the most efficient and independent components that describe a whole set of human faces database this procedure is done by merging the runs of two algorithms based on principal component analysis pca and independent component analysis ica running sequentially this algorithm is applied to face recognition problem simulation results on different databases showed high average success rate of this algorithm compared to others

approximate and exact completion problems for euclidean distance matrices using semidefinite programming abstract a partial pre distance matrix a is a matrix with zero diagonal and with certain elements fixed to given nonnegative values the other elements are considered free the euclidean distance matrix completion problem chooses nonnegative values for the free elements in order to obtain a euclidean distance matrix edm the nearest or approximate euclidean distance matrix problem is to find a euclidean distance matrix edm that is nearest in the frobenius norm to the matrix a when the free variables are discounted in this paper we introduce two algorithms one for the exact completion problem and one for the approximate completion problem both use a reformulation of edm into a semidefinite programming problem sdp the first algorithm is based on an implicit equation for the completion that for many instances provides an explicit solution the other algorithm is based on primal dual interior point methods that exploit the structure and sparsity included are results on maps that arise that keep the edm and sdp cones invariant we briefly discuss numerical tests

formulating robust linear regression estimation as a one class lda criterion discriminative hat matrix linear discriminant analysis such as fisher s criterion is a statistical learning tool traditionally devoted to separating a training dataset into two or even several classes by the way of linear decision boundaries in this paper we show that this tool can formalize the robust linear regression problem as a robust estimator will do more precisely we develop a one class fischer s criterion in which the maximization provides both the regression parameters and the separation of the data in two classes typical data and atypical data or outliers this new criterion is built on the statistical properties of the subspace decomposition of the hat matrix from this angle we improve the discriminative properties of the hat matrix which is traditionally used as outlier diagnostic measure in linear regression naturally we call this new approach discriminative hat matrix the proposed algorithm is fully nonsupervised and needs only the initialization of one parameter synthetic and real datasets are used to study the performance both in terms of regression and classification of the proposed approach we also illustrate its potential application to image recognition and fundamental matrix estimation in computer vision

hyperneural network an efficient model for test generation in digital circuits this paper considers the problem of applying neural network for logic circuit testing and proposes an efficient method based on hyperneural network hnn the hnn uses an energy function that not only considers binary relations but also captures all higher order relations among n neurons we illustrate the hyperneural concept using two formulations first a constraint energy function is defined and the gate model is obtained second the hopfield network is reformulated to generate the gate level hyperneural model the gate level hnn are used to give a mathematical form to the digital circuit that in turn requires optimization techniques to solve the test generation problem we have used iscas 85 benchmark circuits to illustrate the method results are compared with those obtained from podem modem and fan

enhanced fisher discriminant criterion for image recognition many previous studies have shown that image recognition can be significantly improved by fisher linear discriminant analysis flda technique however flda ignores the variation of data points from the same class which characterizes the most important modes of variability of patterns and helps to improve the generalization capability of flda thus the performance of flda on testing data is not good enough in this paper we propose an enhanced fisher discriminant criterion efdc efdc explicitly considers the intra class variation and incorporates the intra class variation into the fisher discriminant criterion to build a robust and efficient dimensionality reduction function efdc obtains a subspace which best detects the discriminant structure and simultaneously preserves the modes of variability of patterns which will result in stable intraclass representation experimental results on four image database show a clear improvement over the results of flda based methods

distributed maximum likelihood sensor network localization we propose a class of convex relaxations to solve the sensor network localization problem based on a maximum likelihood ml formulation this class as well as the tightness of the relaxations depends on the noise probability density function pdf of the collected measurements we derive a computational efficient edge based version of this ml convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge based convex programs locally by communicating only with their close neighbors this algorithm relies on the alternating direction method of multipliers admm it converges to the centralized solution it can run asynchronously and it is computation error resilient finally we compare our proposed distributed scheme with other available methods both analytically and numerically and we argue the added value of admm especially for large scale networks

quadratic projection based feature extraction with its application to biometric recognition this paper presents a novel quadratic projection based feature extraction framework where a set of quadratic matrices is learned to distinguish each class from all other classes we formulate quadratic matrix learning qml as a standard semidefinite programming sdp problem however the conventional interior point sdp solvers do not scale well to the problem of qml for high dimensional data to solve the scalability of qml we develop an efficient algorithm termed dualqml based on the lagrange duality theory to extract nonlinear features to evaluate the feasibility and effectiveness of the proposed framework we conduct extensive experiments on biometric recognition experimental results on three representative biometric recognition tasks including face palmprint and ear recognition demonstrate the superiority of the dualqml based feature extraction algorithm compared to the current state of the art algorithms highlightsa novel quadratic projection based feature extraction framework is developed a set of quadratic matrices is learnt to distinguish each class from other classes quadratic matrix learning qml is formulated as an sdp problem an efficient algorithm is developed to solve qml based on lagrange duality theory experiments on biometric recognition show the effectiveness of our algorithm