Doubly Aligned Incomplete Multi-view Clustering

TL;DR

This paper introduces DAIMC, a novel multi-view clustering method that effectively handles incomplete views by leveraging instance alignment and consensus basis matrices, outperforming existing approaches on real datasets.

Contribution

The paper proposes a doubly aligned incomplete multi-view clustering algorithm using weighted semi-NMF, addressing view incompleteness and view alignment simultaneously.

Findings

DAIMC outperforms existing methods on four real-world datasets.

It effectively handles more than two views with missing instances.

The method reduces the influence of missing data on clustering quality.

Abstract

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of $L_{2,1}$-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.