Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Artificial Intelligence
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-category classification by kernel based nonlinear subspace method
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A formal analysis of why heuristic functions work
Artificial Intelligence
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In Kernel based Nonlinear Subspace (KNS) methods, the subspace dimensions have a strong influence on the performance ofthe subspace classifier In this paper, we propose a new method of systematically and efficiently selecting optimal, or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed as the Overlapping criterion The task of selecting optimal subspace dimensions is equivalent to finding the best ones from a given problem-domain solution space We thus employ the Overlapping criterion of the subspaces as a heuristic function, by which the search space can be pruned to find the best solution to reduce the computation significantly Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy The results especially demonstrate that the computational advantage for large data sets is significant.