On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Atomic Decomposition by Basis Pursuit
SIAM Review
Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online finger-knuckle-print verification for personal authentication
Pattern Recognition
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Palmprint identification based on wide principal lines
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Palmprint matching using feature points and SVD factorisation
Digital Signal Processing
Palmprint recognition method based on a new kernel sparse representation method
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature space by utilizing kernel trick. Since samples in the high dimensional feature space are unknown, we cannot perform KSRC directly. In order to overcome this difficulty, we give the method to solve the problem of sparse representation in the high dimensional feature space. If an appropriate kernel is selected, in the high dimensional feature space, a test sample is probably represented as the linear combination of training samples of the same class more accurately. Therefore, KSRC has more powerful classification ability than SRC. Experiments of face recognition, palmprint recognition and finger-knuckle-print recognition demonstrate the effectiveness of KSRC.