Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance

TL;DR

This paper introduces MIMB, a novel manifold-based method for incomplete multi-view clustering that effectively recovers missing data, balances view importance, and guides clustering through bi-consistency, outperforming existing methods.

Contribution

MIMB is the first to integrate manifold embedding, bi-consistency guidance, and adaptive view weighting for incomplete multi-view clustering.

Findings

MIMB achieves superior clustering accuracy on 6 benchmark datasets.

The method effectively balances view importance and handles missing data.

Experimental results outperform several state-of-the-art baselines.

Abstract

Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple recovery strategy will cause errors and outlying value accumulations, which will affect the performance of the methods. Broadly, the previous methods have not taken the effectiveness of recovered instances into consideration, or cannot flexibly balance the discrepancies between recovered data and original data. To address these problems, we propose a novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization. In particular, MIMB adds reconstruction terms to representation learning by recovering missing instances, which dynamically examines the latent consensus representation. Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Notably, MIMB aims to balance the importance of different views, and introduces an adaptive weight term for each view. Finally, an optimization algorithm with an alternating iteration optimization strategy is designed for final clustering. Extensive experimental results on 6 benchmark datasets are provided to confirm that MIMB can significantly obtain superior results as compared with several state-of-the-art baselines.