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

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

高次元漸近理論の統一的研究

科学研究費助成事業 研究成果報告書：若手研究(B)2014-2017課題番号 : 2680007

Effective Methodologies for Statistical Inference on Microarray Studies

Edited by: Philippe E. Spies