IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast computation of low rank matrix approximations
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
A Theoretical Study on Six Classifier Fusion Strategies
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
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
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Classifier Fusion Using Shared Sampling Distribution for Boosting
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Classification in an informative sample subspace
Pattern Recognition
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
A fast computation of inter-class overlap measures using prototype reduction schemes
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
On optimizing dissimilarity-based classification using prototype reduction schemes
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Self-adaptive classifier fusion for expression-insensitive face recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Feature extraction and evolution based pattern recognition
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Adaptive classifier selection on hierarchical context modeling for robust vision systems
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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In Kernel-based Nonlinear Subspace (KNS) methods, the length of the projections onto the principal component directions in the feature space, is computed using a kernel matrix, K, whose dimension is equivalent to the number of sample data points. Clearly this is problematic, especially, for large data sets. In this paper, we solve this problem by subdividing the data into smaller subsets, and utilizing a Prototype Reduction Scheme (PRS) as a preprocessing module, to yield more refined representative prototypes. Thereafter, a Classifier Fusion Strategy (CFS) is invoked as a postprocessing module, to combine the individual KNS classification results to derive a consensus decision. Essentially, the PRS is used to yield computational advantage, and the CFS, in turn, is used to compensate for the decreased efficiency caused by the data set division. Our experimental results demonstrate that the proposed mechanism significantly reduces the prototype extraction time as well as the computation time without sacrificing the classification accuracy. The results especially demonstrate a significant computational advantage for large data sets within a parallel processing philosophy.