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(m 2), 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.
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Shi, W., Guo, YF. (2009). Nonlinear Component Analysis for Large-Scale Data Set Using Fixed-Point Algorithm. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_16
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DOI: https://doi.org/10.1007/978-3-642-01513-7_16
Publisher Name: Springer, Berlin, Heidelberg
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