Journal
SCANDINAVIAN JOURNAL OF STATISTICS
Volume 47, Issue 3, Pages 899-921Publisher
WILEY
DOI: 10.1111/sjos.12432
Keywords
clustering; geometric representation; HDLSS; microarray; PCA; PC score
Categories
Funding
- Japan Society for the Promotion of Science (JSPS) [26800078, 15H01678, 17K19956]
- Grants-in-Aid for Scientific Research [26800078, 17K19956] Funding Source: KAKEN
Ask authors/readers for more resources
In this article, we consider clustering based on principal component analysis (PCA) for high-dimensional mixture models. We present theoretical reasons why PCA is effective for clustering high-dimensional data. First, we derive a geometric representation of high-dimension, low-sample-size (HDLSS) data taken from a two-class mixture model. With the help of the geometric representation, we give geometric consistency properties of sample principal component scores in the HDLSS context. We develop ideas of the geometric representation and provide geometric consistency properties for multiclass mixture models. We show that PCA can cluster HDLSS data under certain conditions in a surprisingly explicit way. Finally, we demonstrate the performance of the clustering using gene expression datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available