Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Hui Chen, Kunpeng Xu, Lifei Chen and Qingshan Jiang
Additional contact information
Hui Chen: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Kunpeng Xu: Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Lifei Chen: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350007, China
Qingshan Jiang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Mathematics, 2021, vol. 9, issue 14, 1-22

Abstract: Kernel clustering of categorical data is a useful tool to process the separable datasets and has been employed in many disciplines. Despite recent efforts, existing methods for kernel clustering remain a significant challenge due to the assumption of feature independence and equal weights. In this study, we propose a self-expressive kernel subspace clustering algorithm for categorical data (SKSCC) using the self-expressive kernel density estimation (SKDE) scheme, as well as a new feature-weighted non-linear similarity measurement. In the SKSCC algorithm, we propose an effective non-linear optimization method to solve the clustering algorithm’s objective function, which not only considers the relationship between attributes in a non-linear space but also assigns a weight to each attribute in the algorithm to measure the degree of correlation. A series of experiments on some widely used synthetic and real-world datasets demonstrated the better effectiveness and efficiency of the proposed algorithm compared with other state-of-the-art methods, in terms of non-linear relationship exploration among attributes.

Keywords: machine learning; categorical data; similarity; feature selection; kernel density estimation; non-linear optimization; kernel clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/14/1680/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/14/1680/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:14:p:1680-:d:596015

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().