Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Experiments with Random Projection
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Correlation Pattern Recognition
Correlation Pattern Recognition
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Recursive principal components analysis using eigenvector matrix perturbation
EURASIP Journal on Applied Signal Processing
Journal of Cognitive Neuroscience
Pattern Recognition
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
A regularized correntropy framework for robust pattern recognition
Neural Computation
Correntropy-Based document clustering via nonnegative matrix factorization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Multidimensional Systems and Signal Processing
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The minimum average correlation energy (MACE) filter is a well known correlation filter for object recognition. Recently, a nonlinear extension to the MACE filter using the correntropy function in feature space has been introduced. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing higher order moment information of signal structure. Since the MACE is a spatial matched filter for an image class, the correntropy MACE (CMACE) can potentially improve its performance. Both the MACE and CMACE are basically memory-based algorithms and due to the high dimensionality of the image data, the computational cost of the CMACE filter is one of the critical issues in practical applications. We propose to use a dimensionality reduction method based on random projections (RP), which has emerged as a powerful method for dimensionality reduction in machine learning. We apply the CMACE filter with random projection (CMACE-RP) to face recognition and show that it indeed outperforms the traditional linear MACE in both generalization and rejection abilities with small degradation in performance, but great savings in storage and computational complexity.