An adaptive weighted self-representation method for incomplete multi-view clustering

TL;DR

This paper introduces an adaptive weighted self-representation method for incomplete multi-view clustering that effectively handles missing data by adaptively tuning weights and imposing low rank and smoothness constraints, improving clustering accuracy.

Contribution

The proposed AWSR method innovatively adaptively adjusts weights based on instance views and recovery process, addressing limitations of existing IMC approaches.

Findings

Outperforms eight existing methods in clustering accuracy, NMI, and Purity.

Effectively handles missing data with adaptive weighting and low rank constraints.

Theoretical convergence analysis supports the method's reliability.

Abstract

For multi-view data in reality, part of its elements may be missing because of human or machine error. Incomplete multi-view clustering (IMC) clusters the incomplete multi-view data according to the characters of various views of the instances. Recently, IMC has attracted much attention and many related methods have been proposed. However, the existing approaches still need to be developed and innovated in the following aspects: (1) Current methods only consider the differences of different views, while the different influences of instances, as well as distinguishes between missing values and completed values are ignored. (2) The updating scheme for weighting matrix in adaptive weighted algorithms usually relies on an optimization sub-problem, whose optimal solution may not be easy to achieve. (3) The adaptive weighted subspace algorithms that can recover the incomplete data are anchor types. The randomness of the anchor matrix may cause unreliability. To tackle these limitations, we propose an adaptive weighted self-representation (AWSR) subspace method for IMC. The AWSR method tunes the weighting matrix adaptively in accordance with the views of different instances and the recovery process of the missing values. The low rank and smoothness constraints on the representation matrix make the subspace reveal the underlying features of the dataset accurately. We also analyze the convergence property of the block coordinate method for our optimization model theoretically. Numerical performance on five real-world data shows that the AWSR method is effective and delivers superior results when compared to other eight widely-used approaches considering the clustering accuracy (ACC), normalized mutual information (NMI) and Purity.