Information Recovery-Driven Deep Incomplete Multiview Clustering Network

TL;DR

This paper introduces RecFormer, a deep learning model that effectively recovers missing multi-view data and improves clustering performance through a two-stage autoencoder with self-attention and a recurrent graph mechanism.

Contribution

The paper proposes a novel deep incomplete multi-view clustering network with a two-stage autoencoder and recurrent graph mechanism for simultaneous data recovery and clustering.

Findings

RecFormer outperforms existing methods in recovery accuracy.

Visualization confirms effective missing data reconstruction.

Experimental results show improved clustering performance.

Abstract

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.