Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Mixtures of probabilistic principal component analyzers
Neural Computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
An efficient search algorithm for motion data using weighted PCA
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Supervised probabilistic principal component analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting Principal Components in a Two-Stage LDA Algorithm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Weighted and robust learning of subspace representations
Pattern Recognition
Journal of Cognitive Neuroscience
Support Vector Machines for Visualization and Dimensionality Reduction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
An efficient discriminant-based solution for small sample size problem
Pattern Recognition
Semi-supervised Dimension Reduction Using Graph-Based Discriminant Analysis
CIT '09 Proceedings of the 2009 Ninth IEEE International Conference on Computer and Information Technology - Volume 02
A new discriminant principal component analysis method with partial supervision
Neural Processing Letters
A new ranking method for principal components analysis and its application to face image analysis
Image and Vision Computing
Semi-supervised local fisher discriminant analysis for dimensionality reduction
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Principal Component Analysis (PCA) is a multivariate statistical dimensionality reduction method that has been applied successfully in many pattern recognition problems. In the research area of analysis of faces particularly, PCA has been used not only as a pre-processing step to produce accurate analytical model for automated face recognition systems, but also as a conceptual framework for human face coding. Despite the well-known attractive properties of PCA, the traditional approach does not incorporate high level semantics from human reasoning which may steer its subspace computation. In this paper, we propose a method that allows PCA to incorporate such semantics explicitly. It allows an automatic selective treatment of the variables that compose the patterns of interest, performing data feature extraction and dimensionality reduction whenever some high level information in the form of labeled data are available. The method relies on spatial weights calculated, in this work, by separating hyperplanes. Several experiments using 2D frontal face images and different data sets have been carried out to illustrate the usefulness of the method for dimensionality reduction, interpretation, classification and reconstruction of face images.