Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings

Yugo Nakayama, Kazuyoshi Yata and Makoto Aoshima

Journal of Multivariate Analysis, 2021, vol. 185, issue C

Abstract: In this paper, we consider clustering based on the kernel principal component analysis (KPCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons why the Gaussian kernel is effective for clustering high-dimensional data. In addition, we discuss a choice of the scale parameter yielding a high performance of the KPCA with the Gaussian kernel. Finally, we test the performance of the clustering by using microarray data sets.

Keywords: HDLSS; Non-linear PCA; PC score; Radial basis function kernel; Spherical data (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.jmva.2021.104779

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