  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.
