Deep Incomplete Multi-View Multiple Clusterings

TL;DR

This paper introduces DiMVMC, a deep learning framework that simultaneously completes incomplete multi-view data and generates multiple diverse clusterings, outperforming existing methods in clustering quality and diversity.

Contribution

The paper proposes a novel deep incomplete multi-view multiple clustering framework that handles data completion and multiple clusterings with high diversity, a previously underexplored area.

Findings

DiMVMC outperforms state-of-the-art methods in benchmark tests.

It effectively completes incomplete multi-view data.

It generates multiple diverse and high-quality clusterings.

Abstract

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on the predefined clustering criterion, but devising such a criterion that captures what users need is difficult. Due to the multiplicity of multi-view data, we can have meaningful alternative clusterings. In addition, the incomplete multi-view data problem is ubiquitous in real world but has not been studied for multiple clusterings. To address these issues, we introduce a deep incomplete multi-view multiple clusterings (DiMVMC) framework, which achieves the completion of data view and multiple shared representations simultaneously by optimizing multiple groups of decoder deep networks. In addition, it minimizes a redundancy term to simultaneously %uses Hilbert-Schmidt Independence Criterion (HSIC) to control the diversity among these representations and among parameters of different networks. Next, it generates an individual clustering from each of these shared representations. Experiments on benchmark datasets confirm that DiMVMC outperforms the state-of-the-art competitors in generating multiple clusterings with high diversity and quality.