Adaptive multi-view multiple-means clustering via subspace reconstruction. (September 2022)

• Record Type: Journal Article
• Authors: Liu, Wenzhe; Liu, Luyao; Zhang, Yong; Wang, Huibing; Feng, Lin
• Is Part Of: Engineering applications of artificial intelligence. Volume 114(2022)
• Publication Date: 2022-09
• Subjects: Multi-view clustering -- Subspace reconstruction -- Adaptive cooperative learning -- K-multiple-means; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104986
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Clustering is a notable research topic, but it is still challenging when facing massive multi-view data from different ways or multiple feature extractors. The crucial problem is how to promote cooperative learning between views via subspace reconstruction while preserving the underlying geometric structure of data. Moreover, most existing methods habitually utilize K-means to achieve the final results, which is not conducive to dealing with intricate non-convex patterns in multi-perspective data. Based on the above consideration, in this paper, we present a fresh multi-view clustering approach called Adaptive Multi-view Multiple-Means Clustering via Subspace Reconstruction(AM 2 CSR). AM 2 CSR aims to simultaneously capture compatible, complementary, geometric, and discrimination information among multiple views. Subsequently, a low-rank restriction is forced on the low-dimensional representation to reduce redundancy, and K-Multiple-Means(KMM) is adopted as the clustering technique to achieve satisfying results. Additionally, an effective iteration updating method with a convergence guarantee is applied to settle the optimization matter of AM 2 CSR. Extensive empirical experiments on eight benchmark datasets exhibit the superiority of AM 2 CSR.