Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Self-organizing maps
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Vector Quantization Technique for Nonparametric Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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The subspace method of pattern recognition is a classification technique in which pattern classes are specified in terms of linear subspaces spanned by their respective class-based basis vectors. To overcome the limitations of the linear methods, Kernel based Nonlinear Subspace (KNS) methods have been recently proposed in the literature. In KNS, the kernel Principal Component Analysis (kPCA) has been employed to get principal components, not in an input space, but in a highdimensional space, where the components of the space are nonlinearly related to the input variables.In this paper, we suggest a computationally superior mechanism to solve the problem. Rather than define the matrix K with the whole data set and compute the principal components, we propose that the data be reduced into a smaller representative subset using a Prototype Reduction Scheme (PRS). Our experimental results demonstrate that the proposed mechanism dramatically reduces the computation time without sacrificing the classification accuracy for samples involving real-life data sets as well as artificial data sets. The results especially demonstrate the computational advantage for large data sets, such as those involved in data mining and text categorization applications.