Multi-level Feature Learning for Contrastive Multi-view Clustering
This work addresses a key bottleneck in multi-view clustering for data analysis applications, representing an incremental improvement over existing methods.
The paper tackles the conflict between learning consistent common semantics and reconstructing view-private information in multi-view clustering by proposing a multi-level feature learning framework that separates these objectives into different feature spaces. The method achieves state-of-the-art clustering effectiveness on public datasets.
Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.