Density-sensitive Robust Fuzzy Kernel Principal Component Analysis technique

摘要

In order to deal with the sensitivity of traditional kernel principal component analysis (KPCA) to the outliers and high computational complexity of the other existing robust KPCAs, a novel density-sensitive Robust Fuzzy Kernel Principal Component Analysis (DRF-KPCA) is proposed in this paper. First, the initial membership degree of the sample is determined by introducing the relative density. Second, the membership degree is formulated based on reconstruction error and updated by the optimal gradient descent approach, which can effectively solve the problem of the principal component skewing caused by the sensitivity of KPCA to the outliers. Ultimately, the computational complexity and running time of DRF-KPCA are significantly reduced by simplifying the calculation formula of the reconstruction error. In the experiments, as compared with KPCA and other modified approaches on both the datasets with outliers and the datasets without outliers, DRF-KPCA is evaluated to effectively eliminate the impacts of the outliers on the performance with low computational complexity. In addition, the influence of parameters on the performance of DRF-KPCA is also analyzed in detail and the suggestions of determination on the optimal coefficients are given. Eventually, the comparison results with the-state-of-art techniques on UCI benchmark datasets and other high-dimensional classification datasets demonstrate that the performance of DRF-KPCA is significantly improved. (C) 2018 Elsevier B.V. All rights reserved.