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

被引：13
|
作者：
Nakayama, Yugo [1 ]
Yata, Kazuyoshi [2 ]
Aoshima, Makoto [2 ]
机构：
[1] Kyoto Univ, Grad Sch Informat, Kyoto, Japan
[2] Univ Tsukuba, Inst Math, Tsukuba, Ibaraki 3058571, Japan
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2021年 / 185卷
关键词：
HDLSS; Non-linear PCA; PC score; Radial basis function kernel; Spherical data; STATISTICAL SIGNIFICANCE; GEOMETRIC REPRESENTATION; DATA CLASSIFICATION; PCA;
D O I：
10.1016/j.jmva.2021.104779
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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. (C) 2021 The Author(s). Published by Elsevier Inc.
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收藏
页数：15
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