Sparse Clustering Algorithm Based on Multi-Domain Dimensionality Reduction Autoencoder

Yu Kang, Erwei Liu, Kaichi Zou, Xiuyun Wang and Huaqing Zhang ()
Additional contact information
Yu Kang: College of Science, China University of Petroleum, Qingdao 266580, China
Erwei Liu: College of Science, China University of Petroleum, Qingdao 266580, China
Kaichi Zou: College of Computer, China University of Petroleum, Qingdao 266580, China
Xiuyun Wang: College of Computer, China University of Petroleum, Qingdao 266580, China
Huaqing Zhang: College of Science, China University of Petroleum, Qingdao 266580, China

Mathematics, 2024, vol. 12, issue 10, 1-16

Abstract: The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering results. In order to address these issues, we propose a sparse clustering algorithm based on a multi-domain dimensionality reduction model. This method achieves high-dimensional clustering by integrating the sparse reconstruction process and sparse L1 regularization into a deep autoencoder model. A sparse reconstruction module is designed based on the L1 sparse reconstruction of features under different domains to reconstruct the data. The proposed method mainly contributes in two aspects. Firstly, the spatial and frequency domains are combined by taking into account the spatial distribution and frequency characteristics of the data to provide multiple perspectives and choices for data analysis and processing. Then, a neural network-based clustering model with sparsity is conducted by projecting data points onto multi-domains and implementing adaptive regularization penalty terms to the weight matrix. The experimental results demonstrate superior performance of the proposed method in handling clustering problems on high-dimensional datasets.

Keywords: high dimensional; clustering; multi-domain; sparsity; regularization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/10/1526/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/10/1526/ (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:12:y:2024:i:10:p:1526-:d:1394211

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 ().