Research Area:  Machine Learning

Abstract:

Multi-view data effectively model and characterize the underlying complex systems, and multi-view clustering is of great significance for revealing the mechanisms of systems, which groups objects into different clusters with high intra-cluster and low inter-cluster similarity for all views. Current algorithms are criticized for undesirable performance because they solely focus on either the shared features or correlation of objects, failing to address the heterogeneity and structural constraint of various views. To overcome these problems, a novel M ulti-view C lustering with S elf-representation and S tructural C onstraint (MCSSC) is proposed, which is a network-based method by fusing matrix factorization and low-rank representation of various views. Specifically, to remove heterogeneity of multi-view data, a network is constructed for each view, which casts the multi-view clustering into the multi-layer networks clustering problem. To extract the shared features of multiple views, MCSSC factorizes matrices associated with networks by projecting them into a common space and jointly learns an affinity graph for objects in multiple views with self-representation. To facilitate the clustering, the structural constraint is imposed on the affinity graph, where the clusters are identified. Extensive experiments demonstrate that MCSSC significantly outperforms the state-of-the-art in terms of accuracy, implying that the superiority of the proposed method.

Keywords:  
Feature extraction
Clustering algorithms
Matrix decomposition
Approximation algorithms
Correlation
Network topology
Object recognition
Complex systems
Modeling

Author(s) Name:  Xiaowei Gao; Xiaoke Ma; Wensheng Zhang

Publisher name:  IEEE

DOI:  10.1109/TBDATA.2021.3128906

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9618849/