Robust kernel principal component analysis with optimal mean
- PMID: 35598403
- DOI: 10.1016/j.neunet.2022.05.005
Robust kernel principal component analysis with optimal mean
Abstract
The kernel principal component analysis (KPCA) serves as an efficient approach for dimensionality reduction. However, the KPCA method is sensitive to the outliers since the large square errors tend to dominate the loss of KPCA. To strengthen the robustness of KPCA method, we propose a novel robust kernel principal component analysis with optimal mean (RKPCA-OM) method. RKPCA-OM not only possesses stronger robustness for outliers than the conventional KPCA method, but also can eliminate the optimal mean automatically. What is more, the theoretical proof proves the convergence of the algorithm to guarantee that the optimal subspaces and means are obtained. Lastly, exhaustive experimental results verify the superiority of our method.
Keywords: Kernel principal component analysis; Optimal mean; Robust principal component analysis.
Copyright © 2022 Elsevier Ltd. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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