Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

TL;DR

This paper introduces UIMC, a novel view evolution-based clustering method for unbalanced incomplete multi-view data, effectively handling diverse incompleteness levels and noise, inspired by biological evolution.

Contribution

It proposes the first view evolution scheme and weighted multi-view subspace clustering for unbalanced incomplete multi-view data, improving clustering performance significantly.

Findings

UIMC outperforms state-of-the-art methods by up to 40% in clustering metrics.

The method effectively handles unbalanced incompleteness and noise.

Experimental results validate the robustness and superiority of UIMC.

Abstract

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using the previous methods for clustering. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate these unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view problem; 2) it designs the low-rank and robust representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods.