Numerical recipes in C (2nd ed.): the art of scientific computing
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Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
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Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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The Journal of Machine Learning Research
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The Journal of Machine Learning Research
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IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient algorithm for generalized discriminant analysis using incomplete Cholesky decomposition
Pattern Recognition Letters
KPCA for semantic object extraction in images
Pattern Recognition
Density-weighted nyström method for computing large kernel eigensystems
Neural Computation
Feature Extraction Using Linear and Non-linear Subspace Techniques
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
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ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Neurocomputing
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This paper provides a new insight into unsupervised feature extraction techniques based on kernel subspace models. The data projected onto kernel subspace models are new data representations which might be better suited for classification. The kernel subspace models are always described exploiting the dual form for the basis vectors which requires that the training data must be available even during the test phase. By exploiting an incomplete Cholesky decomposition of the kernel matrix, a computationally less demanding implementation is proposed. Online benchmark data sets allow the evaluation of these feature extraction methods comparing the performance of two classifiers which both have as input either the raw data or the new representations.