The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental nonlinear PCA for classification
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
An Improved Algorithm for Kernel Principal Component Analysis
Neural Processing Letters
Accelerated Kernel Feature Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Signal Processing
A novel incremental principal component analysis and its application for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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A semi-supervised learning method, the on-line accelerated kernel feature analysis (On-line AKFA) is presented. In On-line AKFA, features are extracted while data are being fed to the algorithm in small batches as the algorithm proceeds. The paper compares and contrasts the use of On-line AKFA and Off-line AKFA in CT colonography. On-line AKFA provides the flexibility to allow the feature space to dynamically adjust to changes in the input data with time during the training phase. The computational time, reconstruction accuracy, projection variance, and classification performance of the proposed method are experimentally evaluated for kernel principal component analysis (KPCA), Off-line AKFA, and On-line AKFA. Experimental results demonstrate a significant reduction in computation time for On-line AKFA compared to the other feature extraction methods considered in this paper.