Robust kernel principal component analysis with optimal mean. (August 2022)

• Record Type: Journal Article
• Authors: Li, Pei; Zhang, Wenlin; Lu, Chengjun; Zhang, Rui; Li, Xuelong
• Is Part Of: Neural networks. Volume 152(2022)
• Page Start: 347
• Page End: 352
• Publication Date: 2022-08
• Subjects: Kernel principal component analysis -- Robust principal component analysis -- Optimal mean; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.05.005
• Languages: English
• ISSNs: 0893-6080

Abstract

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.