Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moment-Preserving Piecewise Linear Approximations of Signals and Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Intrinsic generalization analysis of low dimensional representations
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Optimal Linear Representations of Images for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
Tools for application-driven linear dimension reduction
Neurocomputing
Kernel methods for nonlinear discriminative data analysis
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning multiview face subspaces and facial pose estimation using independent component analysis
IEEE Transactions on Image Processing
Structure preserving non-negative matrix factorization for dimensionality reduction
Computer Vision and Image Understanding
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Linear techniques are widely used to reduce the dimension of image representation spaces in applications such as image indexing and object recognition. Optimal Component Analysis (OCA) is a method that addresses the problem of learning an optimal linear representation for a particular classification task. The problem is formulated in the framework of optimization on a Grassmann manifold and treated with stochastic gradient methods intrinsic to the manifold. OCA has been successfully applied to image classification problems arising in a variety of contexts. However, as the search space is typically very high dimensional, OCA optimization often requires a large number of iterations, each involving extensive computations that make the algorithm somewhat costly to implement. In this paper, we propose a two-stage method, which we refer to as two-stage OCA, that improves the search efficiency by orders of magnitude without compromising the quality of the estimation. In fact, extensive experiments using face and object classification datasets indicate that the proposed method often leads to more accurate classification than the original OCA since it is not as prone to over-fitting. Two-stage OCA also leads to substantial improvement in classification performance as compared to other linear dimension reduction methods.