Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering

TL;DR

This paper introduces CoCo-IMC, a novel framework for incomplete multi-view clustering that balances complementarity and consistency through delayed activation, improving data recovery and clustering performance.

Contribution

It presents the first theoretical integration of delayed activation into incomplete data recovery and multi-view clustering, enhancing the balance of complementarity and consistency.

Findings

Outperforms 12 state-of-the-art methods on four datasets.

Effectively balances complementarity and consistency in incomplete data.

Proves the theoretical benefits of delayed activation in clustering.

Abstract

This paper study one challenging issue in incomplete multi-view clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets.