learning eigenfunctions links spectral embedding and kernel pca in this letter we show a direct relation between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem learning the principal eigenfunctions of an operator defined from a kernel and the unknown data generating density whereas spectral embedding methods provided only coordinates for the training points the analysis justifies a simple extension to out of sample examples the nystrom formula for multidimensional scaling mds spectral clustering laplacian eigenmaps locally linear embedding lle and isomap the analysis provides for all such spectral embedding methods the definition of a loss function whose empirical average is minimized by the traditional algorithms the asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn experiments with lle isomap spectral clustering and mds show that this out of sample embedding formula generalizes well with a level of error comparable to the effect of small perturbations of the training set on the embedding

certain families of series associated with the hurwitz lerch zeta function abstract the history of problems of evaluation of series associated with the riemann zeta function can be traced back to christian goldbach 1690 1764 and leonhard euler 1707 1783 many different techniques to evaluate various series involving the zeta and related functions have since then been developed the authors show how elegantly certain families of series involving the hurwitz lerch zeta function can be evaluated by starting with a single known identity for the hurwitz lerch zeta function some of the special cases of these series identities are also shown to lead to certain known families of summation formulas involving the hurwitz zeta function

gtm the generative topographic mapping latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent or hidden variables a familiar example is factor analysis which is based on a linear transformations between the latent space and the data space in this paper we introduce a form of non linear latent variable model called the generative topographic mapping for which the parameters of the model can be determined using the em algorithm gtm provides a principled alternative to the widely used self organizing map som of kohonen 1982 and overcomes most of the significant limitations of the som we demonstrate the performance of the gtm algorithm on a toy problem and on simulated data from flow diagnostics for a multi phase oil pipeline

double series representation of bounded signals series representations of the form f t approximately sigma sub n infinity sup infinity sigma sub k infinity sup infinity a sub n k nu t n sub e sup 2 pi kts for bounded signals f t are studied as are conditions on the unit function nu t such that coefficients a sub n k reveal the energy content of f t in the time interval n 1 2

integrals involving a function associated with the euler maclaurin summation formula the authors first evaluate sums of certain series involving the riemann zeta function by using the theory of the double gamma function with or without the use of these results the authors then establish seberal integrals involving a certain periodic function which is associated for example with the euler maclaurin summation formula finally by introducing some new mathematical constants several families of definite integrals involving logarithms of gamma functions are considered relevant connections of the results presented here with those given in earlier works are also pointed out

sums associated with the zeta function abstract we evaluate the sums of certain classes of series involving the riemann zeta function by using the theory of the double gamma function which has recently been revived in the study of determinants of laplacians relevant connections with various known results are also pointed out

gabor representation of generalized functions this paper deals with gabor representation of generalized functions by this we mean that a given function is to be expanded in a series involving gabor functions that are located in the points of a given lattice in the time frequency plane we shall mainly consider lattices in which the area of the elementary cells of the lattice equals 1 in 1946 gabor used these expansions for the simultaneous analysis of signals in time and frequency cf 181 gabor stated that the above mentioned expansions exist for every reasonable signal and that the coefficients in the expansion are uniquely determined by the signal this statement is true when interpreted carefully and the aim of this paper is to find out what kind of generalized functions are sufficiently well behaved to allow a development in a gabor series we shall also consider the question of the uniqueness of the coefficients in the expansions as well as questions concerning convergence it turns out that the uniqueness questions can be handled with the aid of the main results of io in particular 2 12 and 2 13 the questions on the existence of expansions are harder to answer and require special care depending on the particular function we consider lpfunctions 1 p 2 in detail and show existence of gabor representation for tempered distribution in general

similarity preserving principal curve an optimal 1 d feature extractor for data representation this paper discusses the problem of what kind of learning model is suitable for the tasks of feature extraction for data representation and suggests two evaluation criteria for nonlinear feature extractors reconstruction error minimization and similarity preservation based on the suggested evaluation criteria a new type of principal curve similarity preserving principal curve sppc is proposed sppcs minimize the reconstruction error under the condition that the similarity between similar samples are preserved in the extracted features thus giving researchers effective and reliable cognition of the inner structure of data sets the existence and properties of sppcs are analyzed a practical learning algorithm is proposed and high dimensional extensions of sppcs are also discussed experimental results show the virtues of sppcs in preserving inner structures of data sets and discovering manifolds with high nonlinearity

hybrid dimensionality reduction method based on support vector machine and independent component analysis this paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk supervised criterion and data independence unsupervised criterion classification accuracy is used as a metric to evaluate the performance of the method by minimizing the structural risk projection originated from the decision boundaries directly improves the classification performance from a supervised perspective from an unsupervised perspective projection can also be obtained based on maximum independence among features or attributes in data to indirectly achieve better classification accuracy over more intrinsic representation of the data orthogonality interrelates the two sets of projections such that minimum redundancy exists between the projections leading to more effective dimensionality reduction experimental results show that the proposed hybrid dimensionality reduction method that satisfies both criteria simultaneously provides higher classification performance especially for noisy data sets in relatively lower dimensional space than various existing methods

some mathematical constants the theory of multiple gamma functions was studied in about 1900 and has been revived in the study of determinants of laplacians in the middle or so of 1980s there are some classes of mathematical constants involved naturally in the gamma and multiple gamma functions here we introduce those known mathematical constants associated with the gamma and multiple gamma functions and will show how they are involved if possible

transforms associated to square integrable group representations i general results let g be a locally compact group which need not be unimodular let x u x x g be an irreducible unitary representation of g in a hilbert space h u assume that u is square integrable i e that there exists in h u at least one nonzero vector g such that u x g g 2 dx we give here a reasonably self contained analysis of the correspondence associating to every vector f h u the function u x g f on g discussing its isometry characterization of the range inversion and simplest interpolation properties this correspondence underlies many properties of generalized coherent states

learning and design of principal curves principal curves have been defined as self consistent smooth curves which pass through the middle of a d dimensional probability distribution or data cloud they give a summary of the data and also serve as an efficient feature extraction tool we take a new approach by defining principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution the new definition makes it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction our theoretical learning scheme chooses a curve from a class of polygonal lines with k segments and with a given total length to minimize the average squared distance over n training points drawn independently convergence properties of this learning scheme are analyzed and a practical version of this theoretical algorithm is implemented in each iteration of the algorithm a new vertex is added to the polygonal line and the positions of the vertices are updated so that they minimize a penalized squared distance criterion simulation results demonstrate that the new algorithm compares favorably with previous methods both in terms of performance and computational complexity and is more robust to varying data models

filter design and comparison for two fast cwt algorithms the continuous wavelet transform cwt is a powerful technique for signal analysis direct cwt computation by fft requires o n log sub 2 n operations per scale where n is the data length the a trous algorithm and the shensa 1992 algorithm are two fast methods to compute cwt recursively that require only o n operations per scale both of them can be described by the multiresolution analysis mra structure but with different mra filters this paper proposes methods to design the mra filters of the two algorithms to improve their accuracy on cwt computation we begin with the formulation of the cwt computation error using the mra structure the mra filters of the two algorithms are then designed to minimize the error in either algorithm both the lowpass and bandpass mra filters can be optimized the a trous algorithm has closed form solutions for the two filters the shensa algorithm on the other hand has an analytic solution for the bandpass filter only finding the optimum lowpass filter requires a multidimensional numerical search simulation studies show that by using the proposed optimum filters the shensa algorithm in general outperforms the a trous algorithm

initialization of orthogonal discrete wavelet transforms the asymptotic formulae of both the approximation error and the systematic error of a special prefilter projection and the quantitative estimates of the upper bounds of the errors are obtained in addition it is shown that for the daubechies orthogonal wavelet basis the estimated constant is optimal

principal surfaces from unsupervised kernel regression we propose a nonparametric approach to learning of principal surfaces based on an unsupervised formulation of the nadaraya watson kernel regression estimator as compared with previous approaches to principal curves and surfaces the new method offers several advantages first it provides a practical solution to the model selection problem because all parameters can be estimated by leave one out cross validation without additional computational cost in addition our approach allows for a convenient incorporation of nonlinear spectral methods for parameter initialization beyond classical initializations based on linear pca furthermore it shows a simple way to fit principal surfaces in general feature spaces beyond the usual data space setup the experimental results illustrate these convenient features on simulated and real data

certain classes of series involving the zeta function the authors apply the theory of the double gamma function which was recently revived in the study of determinants of laplacians to evaluate some families of series involving the riemann zeta function introducing a presumably new mathematical constant in the theory of the double gamma function they also systematically evaluate a definite integral of the double gamma function and various classes of series associated with zeta functions some of these definite integrals are expressed in terms of quotients of double gamma functions

shared kernel information embedding for discriminative inference latent variable models such as the gplvm and related methods help mitigate overfitting when learning from small or moderately sized training sets nevertheless existing methods suffer from several problems 1 complexity 2 the lack of explicit mappings to and from the latent space 3 an inability to cope with multimodality and 4 the lack of a well defined density over the latent space we propose an lvm called the kernel information embedding kie that defines a coherent joint density over the input and a learned latent space learning is quadratic and it works well on small data sets we also introduce a generalization the shared kie skie that allows us to model multiple input spaces e g image features and poses using a single shared latent representation kie and skie permit missing data during inference and partially labeled data during learning we show that with data sets too large to learn a coherent global model one can use the skie to learn local online models we use skie for human pose inference

nonlinear component analysis as a kernel eigenvalue problem a new method for performing a nonlinear form of principal component analysis is proposed by the use of integral operator kernel functions one can efficiently compute principal components in high dimensional feature spaces related to input space by some nonlinear map for instance the space of all possible five pixel products in 16 16 images we give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition

fast algorithms for discrete and continuous wavelet transforms several algorithms are reviewed for computing various types of wavelet transforms the mallat algorithm 1989 the a trous algorithm and their generalizations by shensa the goal of this work is to develop guidelines for implementing discrete and continuous wavelet transforms efficiently and to compare the various algorithms obtained and give an idea of possible gains by providing operation counts most wavelet transform algorithms compute sampled coefficients of the continuous wavelet transform using the filter bank structure of the discrete wavelet transform although this general method is already efficient it is shown that noticeable computational savings can be obtained by applying known fast convolution techniques such as the fft fast fourier transform in a suitable manner the modified algorithms are termed fast because of their ability to reduce the computational complexity per computed coefficient from l to log l within a small constant factor for large filter lengths l for short filters smaller gains are obtained fast running fir finite impulse response filtering techniques allow one to achieve typically 30 savings in computations

the discrete wavelet transform wedding the a trous and mallat algorithms two separately motivated implementations of the wavelet transform are brought together it is observed that these algorithms are both special cases of a single filter bank structure the discrete wavelet transform the behavior of which is governed by the choice of filters in fact the a trous algorithm is more properly viewed as a nonorthonormal multiresolution algorithm for which the discrete wavelet transform is exact moreover it is shown that the commonly used lagrange a trous filters are in one to one correspondence with the convolutional squares of the daubechies filters for orthonormal wavelets of compact support a systematic framework for the discrete wavelet transform is provided and conditions are derived under which it computes the continuous wavelet transform exactly suitable filter constraints for finite energy and boundedness of the discrete transform are also derived relevant signal processing parameters are examined and it is observed that orthonormality is balanced by restrictions on resolution

quadrature prefilters for the discrete wavelet transform discrepancies between the discrete wavelet transform and the coefficients of the wavelet series are known to be reducible by initialization of input data prefilters based on lagrange interpolants are derived here for biorthogonal compact support wavelet systems providing exact subspace projection in cases of local polynomial smoothness the resulting divergence acceleration in a nonpolynomial test case is examined irregular sampling rates are also accommodated

krull s theory for the double gamma function abstract the barnes g function g x 1 2 satisfies the functional equation log g x 1 log g x log x we complement w krull s work in bemerkungen zur differenzengleichung g x 1 g x x math nachrichten 1 1948 365 376 with additional results that yield a different characterization of the function g new expansions and sharp bounds for g on x 0 in terms of gamma and digamma functions a new expansion for the gamma function and summation formulae with polygamma functions

convergence properties of the nelder mead simplex method in low dimensions the nelder mead simplex algorithm first published in 1965 is an enormously popular direct search method for multidimensional unconstrained minimization despite its widespread use essentially no theoretical results have been proved explicitly for the nelder mead algorithm this paper presents convergence properties of the nelder mead algorithm applied to strictly convex functions in dimensions 1 and 2 we prove convergence to a minimizer for dimension 1 and various limited convergence results for dimension 2 a counterexample of mckinnon gives a family of strictly convex functions in two dimensions and a set of initial conditions for which the nelder mead algorithm converges to a nonminimizer it is not yet known whether the nelder mead method can be proved to converge to a minimizer for a more specialized class of convex functions in two dimensions

bargmann transform zak transform and coherent states it is well known that completeness properties of sets of coherent states associated with lattices in the phase plane can be proved by using the bargmann representation or by using the kq representation which was introduced by j zak in this paper both methods are considered in particular in connection with expansions of generalized functions in what are called gabor series the setting consists of two spaces of generalized functions tempered distributions and elements of the class s which appear in a natural way in the context of the bargmann transform also a thorough mathematical investigation of the zak transform is given this paper contains many comments and complements on existing literature in particular connections with the theory of interpolation of entire functions over the gaussian integers are given

multiresolution analysis its link to the discrete parameter wavelet transform and its initialization by establishing an equivalence between the discrete parameter dp wavelet transform and the multiresolution analysis mra detail coefficients a simple derivation of the two scale wavelet equations is given mra computes the dp of a signal s t only if the mra inputs s n are samples of the inner product of s t and the scaling function this is seldom true in practice a prefiltering of s n as an approximate initialization is necessary to make the mra detail coefficients closer to the dp coefficients a new prefiltering scheme is proposed and shown to be effective in an example

certain classes of series associated with the zeta function and multiple gamma functions abstract the authors apply the theory of multiple gamma functions which was recently revived in the study of the determinants of the laplacians in order to evaluate some families of series involving the riemann zeta function by introducing a certain mathematical constant they also systematically evaluate this constant and some definite integrals of the triple gamma function various classes of series associated with the zeta function are expressed in closed forms many of these results are also used here to compute the determinant of the laplacian on the four dimensional unit sphere s 4 explicitly

design of prefilters for discrete multiwavelet transforms the pyramid algorithm for computing single wavelet transform coefficients is well known the pyramid algorithm can be implemented by using tree structured multirate filter banks the authors propose a general algorithm to compute multiwavelet transform coefficients by adding proper premultirate filter banks before the vector filter banks that generate multiwavelets the proposed algorithm can be thought of as a discrete vector valued wavelet transform for certain discrete time vector valued signals the proposed algorithm can be also thought of as a discrete multiwavelet transform for discrete time signals the authors then present some numerical experiments to illustrate the performance of the algorithm which indicates that the energy compaction for discrete multiwavelet transforms may be better than the one for conventional discrete wavelet transforms

computation of wavelet coefficients from average samples there exist efficient methods to compute the wavelet coefficients of a function f t from its point samples f t email protected email protected n however in many applications the available samples are average samples of the type f t email protected u t dt where the averaging function u t reflects the characteristic of the acquisition device in this work methods to compute the coefficients in a biorthogonal wavelet system from average samples are studied error estimations are obtained and using them the optimal values for the parameters in the proposed approximation rules are calculated the obtained error estimations can also be applied to the rules that compute the coefficients from point samples and thus these estimations can be used to compare and to choose between the different methods proposed in the literature the methods proposed here also allow us to compute the biorthogonal wavelet coefficients from the coefficients in another biorthogonal wavelet system

wavelet coefficient computation with optimal prefiltering discrete wavelet transform dwt is often used to approximate wavelet series transform wst and continuous wavelet transform cwt since it can be computed numerically in this research we first study the accuracy of the computed dwt coefficients obtained from the shensa see ibid vol 40 no 10 p 2464 2482 1992 algorithm as an approximate of the wst coefficients based on the accuracy analysis we then propose a procedure to design optimal fir prefilters used in the shensa algorithm to reduce the approximation error finally numerical examples are presented to demonstrate the performance of the optimal fir prefilters

asymptotic expansions of the double zeta function abstract the double zeta function of barnes 2 v z w is considered for large and small values of z and w with w 0 arg z r v 2 and r z 0 to the whole complex z plane and complex v plane with arg z

ondelettes and phase cell cluster expansions a vindication y meyer has recently developed a particularly useful o n basis forl2 r d expansions using this basis i e expansions into ondelettes or wavelets have yielded important new results in soft and hard analysis the expansion into ondelettes of a boson scalar field naturally leads to phase cell cluster expansions a formalism already developed by the authors using other related bases adoption of ondelettes expansions into the phase cell program gives improvements of some extant results and excises an early error

laplacian eigenmaps for dimensionality reduction and data representation one of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data we consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space drawing on the correspondence between the graph laplacian the laplace beltrami operator on the manifold and the connections to the heat equation we propose a geometrically motivated algorithm for representing the high dimensional data the algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering some potential applications and illustrative examples are discussed

a unified model for probabilistic principal surfaces principal curves and surfaces are nonlinear generalizations of principal components and subspaces respectively they can provide insightful summary of high dimensional data not typically attainable by classical linear methods solutions to several problems such as proof of existence and convergence faced by the original principal curve formulation have been proposed in the past few years nevertheless these solutions are not generally extensible to principal surfaces the mere computation of which presents a formidable obstacle consequently relatively few studies of principal surfaces are available we previously 2000 proposed the probabilistic principal surface pps to address a number of issues associated with current principal surface algorithms pps uses a manifold oriented covariance noise model based on the generative topographical mapping gtm which can be viewed as a parametric formulation of kohonen s self organizing map building on the pps we introduce a unified covariance model that implements pps 0 1 by varying the clamping parameter spl alpha then we comprehensively evaluate the empirical performance of pps gtm and the manifold aligned gtm on three popular benchmark data sets it is shown in two different comparisons that the pps outperforms the gtm under identical parameter settings convergence of the pps is found to be identical to that of the gtm and the computational overhead incurred by the pps decreases to 40 percent or less for more complex manifolds these results show that the generalized pps provides a flexible and effective way of obtaining principal surfaces

wavelet packet transform initialization for piecewise polynomial and bandlimited inputs this paper considers the problem of initializing the wavelet packet transform when the analog input x t is either bandlimited or else is piecewise polynomial on the dyadic intervals this is accomplished by the construction of certain prefilter sequences that are applied to the samples x sub k x sub k x kt t is the sampling period in the piecewise polynomial case the prefilter sequences are computed with the aid of the interpolatory graphical display algorithm igda which is used to compute the so called incremental moment functions of the scaling function spl phi t on the dyadic points the moments of spl phi t are also employed

regularization free principal curve estimation principal curves and manifolds provide a framework to formulate manifold learning within a statistical context principal curves define the notion of a curve passing through the middle of a distribution while the intuition is clear the formal definition leads to some technical and practical difficulties in particular principal curves are saddle points of the mean squared projection distance which poses severe challenges for estimation and model selection this paper demonstrates that the difficulties in model selection associated with the saddle point property of principal curves are intrinsically tied to the minimization of the mean squared projection distance we introduce a new objective function facilitated through a modification of the principal curve estimation approach for which all critical points are principal curves and minima thus the new formulation removes the fundamental issue for model selection in principal curve estimation a gradient descent based estimator demonstrates the effectiveness of the new formulation for controlling model complexity on numerical experiments with synthetic and real data

series involving the zeta function and multiple gamma functions the theory of multiple gamma functions which was recently revived in the study of the determinants of the laplacians was applied in several earlier works in order to evaluate some families of series involving the riemann zeta function as well as to compute the determinants of the laplacians here in the present paper the authors address the converse problem and apply various known or new formulas for series associated with the zeta and related functions with a view to developing the corresponding theory of multiple gamma functions and then using these series to compute the determinants of the laplacians on the n dimensional unit sphere s n n 5 6 7 explicitly

sampling approximation of smooth functions via generalized coiflets we present the sampling approximation power of a newly constructed class of compactly supported orthonormal wavelets called generalized coiflets we study the accuracy of generalized coiflets based sampling approximation of smooth functions by developing convergence rates for the pointwise approximation error as well as its l sup p norm we show i that the l sup 2 error due to the approximation of expansion coefficients by function samples is asymptotically negligible compared with that due to projection and ii that generalized coiflets can achieve asymptotically better approximation than the original coiflets

spectral functions special functions and the selberg zeta function the functional determinant of an eigenvalue sequence as defined by zeta regularization can be simply evaluated by quadratures we apply this procedure to the selberg trace formula for a compact riemann surface to find a factorization of the selberg zeta function into two functional determinants respectively related to the laplacian on the compact surface itself and on the sphere we also apply our formalism to various explicit eigenvalue sequences reproducing in a simpler way classical results about the gamma function and the barnesg function concerning the latter our method explains its connection to the selberg zeta function and evaluates the related glaisher kinkelin constanta

a block spin construction of ondelettes part i lemarie functions using block spin assignments we construct anl2 orthonormal basis consisting of dyadic scalings and translates of just a finite number of functions these functions have exponential localization and for even values of a construction parameterm one can make them classcm 1 with vanishing moments up to orderm inclusive such a basis has an important application to phase cell cluster expansions in quantum field theory

the wavelet transform time frequency localization and signal analysis two different procedures for effecting a frequency analysis of a time dependent signal locally in time are studied the first procedure is the short time or windowed fourier transform the second is the wavelet transform in which high frequency components are studied with sharper time resolution than low frequency components the similarities and the differences between these two methods are discussed for both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time frequency density finally the notion of time frequency localization is made precise within this framework by two localization theorems

regularized principal manifolds many settings of unsupervised learning can be viewed as quantization problems the minimization of the expected quantization error subject to some restrictions this allows the use of tools such as regularization from the theory of supervised risk minimization for unsupervised learning this setting turns out to be closely related to principal curves the generative topographic map and robust coding we explore this connection in two ways 1 we propose an algorithm for finding principal manifolds that can be regularized in a variety of ways and 2 we derive uniform convergence bounds and hence bounds on the learning rates of the algorithm in particular we give bounds on the covering numbers which allows us to obtain nearly optimal learning rates for certain types of regularization operators experimental results demonstrate the feasibility of the approach

some integral and asymptotic formulas associated with the hurwitz zeta function the main object of this paper is to present an easily accessible integral representation of the partial sum l u x a 0 n x n a u of the hurwitz zeta function z u a which entails a number of important implications for l u x a and z u a and for their derivatives furthermore a new proof is given for a fundamental summation formula contained in theorem 3 below which is shown to possess numerous further consequences and applications relevant connections of the results obtained here with those considered in many other earlier and recent works are also discussed

multiple gamma and related functions the authors give several new and potentially useful relationships between the multiple gamma functions and other mathematical functions and constants as by products of some of these relationships a classical definite integral due to euler and other definite integrals are also considered together with closed form evaluations of some series involving the riemann and hurwitz zeta functions

rational von neumann lattices a rational von neumann lattice is defined as a lattice in phase space with the constants a and b in the x and p directions given by a ratio of integers zeros of harmonic oscillator functions in the kq representation on such lattices are found it is shown that the number of zeros of the kq function determines the number of states by which a set on a von neumann lattice is overcomplete interesting relations between theta functions are derived on the basis of their connection with the harmonic oscillator states in the kq representation

fast cwt computation at integer scales by the generalized mra structure this article proposes a fast algorithm for continuous wavelet transform cwt at linear scale without decimation by using the generalized multiresolution analysis mra structure the constraints required on the lowpass and bandpass filters in the generalized mra structure are derived a possible solution for the lowpass filters and a least squares design of the bandpass filters are given the computational complexity of the algorithm is o n per scale where n is the data length the fast algorithm is verified by computer simulations

some q extensions of the apostol bernoulli and the apostol euler polynomials of order n and the multiple hurwitz zeta function abstract in this paper we first investigate several further interesting properties of the multiple hurwitz lerch zeta function n z s a which was introduced recently by choi et al j choi d s jang h m srivastava a generalization of the hurwitz lerch zeta function integral transform spec funct 19 2008 we then introduce and investigate some q extensions of the multiple hurwitz lerch zeta function n z s a the apostol bernoulli polynomials b k n x of order n and the apostol euler polynomials e k n x of order n relevant connections of the results presented here with those obtained in earlier works are also indicated precisely

adaptive principal surfaces abstract we develop a nonlinear generalization of principal components analysis a principal surface of the data is constructed adaptively using some ideas from the mars procedure of friedman we explore applications to curve and surface reconstruction and to data summarization

a new prefilter design for discrete multiwavelet transforms in conventional wavelet transforms prefiltering is not necessary due to the lowpass property of a scaling function this is no longer true for multiwavelet transforms a few research papers on the design of prefilters have appeared but the existing prefilters are usually not orthogonal which often causes problems in coding moreover the condition on the prefilters was imposed based on the first step discrete multiwavelet decomposition we propose a new prefilter design that combines the ideas of the conventional wavelet transforms and multiwavelet transforms the prefilters are orthogonal but nonmaximally decimated they are derived from a very natural calculation of multiwavelet transform coefficients in this new prefilter design multiple step discrete multiwavelet decomposition is taken into account our numerical examples by taking care of the redundant prefiltering indicate that the energy compaction ratio with the geronimo hardin massopust 1994 2 wavelet transform and our new prefiltering is better than the one with daubechies d sub 4 wavelet transform

wavelet transform of multifractals we present the wavelet transform as a mathematical microscope which is well suited for studying the local scaling properties of fractal objects we apply this technique to probability measures on self similar cantor sets and to the golden mean trajectories on two tori at the onset of chaos