Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering
It addresses data incompleteness in multi-view clustering, an incremental improvement for machine learning applications in domains like computer vision.
The paper tackles incomplete multi-view clustering by balancing complementary and consistent information across views, achieving state-of-the-art results in experiments with 12 baselines on four datasets.
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.