Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance
Huibing Wang, Mingze Yao, Yawei Chen, Yunqiu Xu, Haipeng Liu, Wei Jia, Xianping Fu, Yang Wang
TL;DR
This work tackles incomplete multi-view clustering by introducing MIMB, a framework that recovers missing view data and learns a robust consensus representation under bi-consistency guidance. It combines recovery-based representation learning, reverse regularization to enforce cross-view consistency, and manifold embedding to preserve local structure, with an adaptive weighting scheme for views. Empirical results on six benchmark datasets show that MIMB consistently outperforms state-of-the-art baselines across missing-rate scenarios, and analyses confirm its convergence and robustness to parameter settings. The approach offers practical benefits for real-world multi-view tasks where data are frequently incomplete and heterogeneous across sources.
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.