Robust multi-view graph clustering in latent energy-preserving embedding space

Due to the powerful capacity of exploring complementary and consistent information by generating a consensus affinity graph from multi-view data, multi-view graph clustering (MVGC) methods have attracted intensive attention. However, multi-view data is usually existed in high-dimensional space, where redundant and irrelevant features may result in the curse of dimensionality. Moreover, original data often mix with noise and outliers that will destroy the underlying clustering structure, such that unreliable and inaccurate affinity graphs will be generated. To alleviate the aforementioned problems, we propose a novel multi-view latent energy-preserving embedding (MLEE) method, which seamlessly integrates the clean embedding space learning and consensus affinity graph learning into a unified objective function. Concretely, for each view, we first learn the low-dimensional yet clean data by proposing a full-energy projection and recovering method. This can well reduce the redundancy and interference in the data. Furthermore, by leveraging adaptive neighbors graph learning (ANGL), the local manifold structure of the clean embedding data can be implicitly preserved. To integrate the complementary and consistent information of different views, an early-fusion scheme is proposed to directly yield a consensus graph for clustering purpose. Experiments on six benchmark datasets demonstrate that our method achieves state-of-the-art clustering performance. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The proposed multi-view latent energy-preserving embedding (MLEE) method aims to integrate multi-view data and alleviate the issues caused by high-dimensionality, achieving state-of-the-art clustering performance by learning low-dimensional clean data and consensus affinity graph learning.