10.6084/M9.FIGSHARE.16653127.V2
Beomseok Seo
Beomseok
Seo
Lin Lin
Lin
Lin
Jia Li
Jia
Li
Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models
<p>Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with the mixture model parameters can represent the original data effectively and much more compactly. We thus cluster the data using the latent states and select variables according to the relationship between the states and the clusters. As true class labels are unknown in the unsupervised setting, we first generate more refined clusters, namely, semi-clusters, for variable selection and then determine the final clusters based on the dimension reduced data. Experiments on simulated and real data show that the new method is highly competitive in terms of clustering accuracy compared with several widely used methods. <a href="https://doi.org/10.1080/10618600.2021.1982724" target="_blank">Supplementary materials</a> for this article are available online.</p>
Genetics
Pharmacology
Biotechnology
Chemical Sciences not elsewhere classified
Immunology
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Taylor & Francis
2021
2021-11-17
2024-02-21
Dataset
18264994 Bytes
10.6084/m9.figshare.16653127
10.1080/10618600.2021.1982724
CC BY 4.0