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ISNN
2009
Springer

Nonlinear Component Analysis for Large-Scale Data Set Using Fixed-Point Algorithm

10 years 5 months ago
Nonlinear Component Analysis for Large-Scale Data Set Using Fixed-Point Algorithm
Abstract. Nonlinear component analysis is a popular nonlinear feature extraction method. It generally uses eigen-decomposition technique to extract the principal components. But the method is infeasible for large-scale data set because of the storage and computational problem. To overcome these disadvantages, an efficient iterative method of computing kernel principal components based on fixed-point algorithm is proposed.The kernel principle components can be iteratively computed without the eigen-decomposition. The space and time complexity of proposed method is reduced to o(m) and o(m2 ), respectively, where m is the number of samples. More important, it still can be used even if traditional eigen-decomposition technique cannot be applied when faced with the extremely large-scale data set. The effectiveness of proposed method is validated from experimental results.
Weiya Shi, Yue-Fei Guo
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ISNN
Authors Weiya Shi, Yue-Fei Guo
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