Robust Spectral Clustering via Low-Rank Sample Representation. (29th April 2022)

• Record Type: Journal Article
• Authors: Liang, Hao; Guan, Hai-Tang; Abhadiomhen, Stanley Ebhohimhen; Yan, Li
• Other Names: Daneshvar Rouyendegh (B. Erdebilli) Babak Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2022(2022)
• Publication Date: 2022-04-29
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2022/7540956
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data's self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coefficient of two samples, which are similar in original high-dimensional space, is close to maintaining the samples' neighborhood structure in the low-dimensional space. As a result, the proposed method obtains a clustering structure directly through the low-rank coefficient matrix to guarantee optimal clustering performance. A wide range of experiments shows that the proposed method is superior to compared state-of-the-art methods.