Supervised probabilistic principal component analysis
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e., in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S2PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e., in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S2PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.

title: A Subspace Kernel for Nonlinear Feature Extraction
abstract: Kernel based nonlinear Feature Extraction (KFE) or dimensionality reduction is a widely used preprocessing step in pattern classification and data mining tasks. Given a positive definite kernel function, it is well known that the input data are implicitly mapped to a feature space with usually very high dimensionality. The goal of KFE is to find a low dimensional subspace of this feature space, which retains most of the information needed for classification or data analysis. In this paper, we propose a subspace kernel based on which the feature extraction problem is transformed to a kernel parameter learning problem. The key observation is that when projecting data into a low dimensional subspace of the feature space, the parameters that are used for describing this subspace can be regarded as the parameters of the kernel function between the projected data. Therefore current kernel parameter learning methods can be adapted to optimize this parameterized kernel function. Experimental results are provided to validate the effectiveness of the proposed approach.

title: A Direct Method for Building Sparse Kernel Learning Algorithms
abstract: Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting kernel machine is explicitly controlled while at the same time performance is kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the support vectom machine results in a concrete algorithm for building sparse large margin classifiers. These classifiers essentially find a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.

title: Building Sparse Large Margin Classifiers
abstract: This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint explicitly controls the sparseness of the classifier and an approach is provided to solve the formulated problem. When considering the dual of this problem. it can be seen that building an SLMC is equivalent to constructing an SVM with a modified kernel function. Further analysis of this kernel function indicates that the proposed approach essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace different classes of data are linearly well separated. Experimental results over several classification benchmarks show that in most cases the proposed approach outperforms the state-of-art sparse learning algorithms.

title: Data hiding in curves for collusion-resistant digital fingerprinting
abstract: This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence in the coordinates of the B-spline control points. We demonstrate through experiments the robustness of the proposed data hiding algorithm against printing-and-scanning and collusions, and show its feasibility for collusion-resistant fingerprinting of topographic maps as well as writings/drawings from pen-based input devices.

title: Fingerprinting Curves
abstract: This paper presents a new method for robust data hiding in curves and highlights potential applications including digital fingerprinting of map document for trace and track purposes. We parameterize a curve using the B-spline model and add a spread spectrum sequence to the coordinates of the B-spline control points. In order to achieve robust fingerprint detection, we propose an iterative alignment-minimization algorithm to perform curve registration and deal with the non-uniqueness of B-spline control points. We show through experiments the robustness of our method against various attacks such as collusion, geometric transformations and perturbation, printing-and-scanning, and some of their combinations. We demonstrate the feasibility of our method for fingerprinting topographic maps as well as writings and drawings. The extension from hiding data in 2D topographic maps to 3D elevation data sets is also discussed.

title: Balancing productivity and consumer satisfaction for profitability: Statistical and fuzzy regression analysis
abstract: This paper examines the relationships among productivity, consumer satisfaction and profitability using the conventional statistical regression and the new fuzzy regression approaches. For service firms in the context of Hong Kong, we verify the profit hypothesis that individually both productivity and consumer satisfaction are positively linked to profitability as well as the tradeoff hypothesis that aggregately there are negative interactions between productivity and consumer satisfaction for enhancing profitability. Hence service firms should balance their efforts in productivity and consumer satisfaction, possibly by employing appropriate information technologies to improve productivity while without hurting consumer satisfaction, to optimize their profitability. The study takes advantage of the Hong Kong Consumer Satisfaction Index and deliberately focuses on total rather than partial productivity. Several models are first estimated using the ordinary least squares (OLS) method and the results generally support the two hypotheses, but the OLS approach also leaves two puzzles that estimates of the regression coefficients are: (1) not significant before considering the interactions between consumer satisfaction and productivity but significant after introducing the interaction term, and (2) significant although sample data for productivity and the interaction term are highly correlated. These puzzles, together with the observed imprecision in productivity and profitability measurements and especially the subjectivity in measuring consumer satisfaction, lead us to adopt the fuzzy linear regression (FLR) techniques to further examine the two hypotheses. The popular FLR model continues to favor our research hypotheses but fail to offer any additional insights into the examined relationships over the OLS models. We then propose a revised FLR model which, in addition to reconfirming the hypotheses, does help to explain the encountered puzzles and fuzziness, and hence suggests an encouraging methodology for marketing.

title: Searcher performance in question answering
abstract: There are many tasks that require information finding. Some can be largely automated, and others greatly benefit from successful interaction between system and searcher. We are interested in the task of answering questions where some synthesis of information is required-the answer would not generally be given from a single passage of a single document. We investigate whether variation in the way a list of documents is delivered affected searcher performance in the question answering task. We will show that there is a significant difference in performance using a list customized to the task type, compared with a standard web-engine list. This indicates that paying attention to the task and the searcher interaction may provide substantial improvement in task performance.

title: Communication-friendly encryption of multimedia
abstract: This paper discusses encryption operations that selectively encrypt content-carrying segments of multimedia data stream. We propose and analyze three techniques that work in different domains, namely, a syntax-aware selective bitstream encryption tool with bit stuffing, a generalized index mapping encryption tool with controlled overhead and an intra-bitplane encryption tool compatible with fine granularity scalable coding. The designs of these proposed encryption operations take into consideration the inherent structure and syntax of multimedia sources and have improved friendliness to communications, compression and computation.