Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity

TL;DR

This paper introduces a hierarchical multiple kernel K-Means algorithm with sparse connectivity that enhances feature fusion and improves clustering performance by controlling information exchange between layers.

Contribution

It proposes a novel sparse connectivity strategy in hierarchical multiple kernel clustering, improving upon full connectivity by better fusing features and enhancing clustering accuracy.

Findings

Sparse connectivity improves clustering performance.

The proposed method outperforms fully connected strategies.

Local feature fusion enhances discriminative information.

Abstract

Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function. In the hierarchical multiple kernel clustering (HMKC) algorithm, sample features are extracted layer by layer from a high-dimensional space to maximize the retention of effective information. However, information interaction between layers is often ignored. In this model, only corresponding nodes in adjacent layers exchange information; other nodes remain isolated, and if full connectivity is adopted, the diversity of the final consistency matrix is reduced. Therefore, this paper proposes a hierarchical multiple kernel K-Means (SCHMKKM) algorithm based on sparse connectivity, which controls the assignment matrix to achieve sparse connections through a sparsity rate, thereby locally fusing the features obtained by distilling information between layers. Finally, we conduct cluster analysis on multiple datasets and compare it with the fully connected hierarchical multiple kernel K-Means (FCHMKKM) algorithm in experiments. It is shown that more discriminative information fusion is beneficial for learning a better consistent partition matrix, and the fusion strategy based on sparse connection outperforms the full connection strategy.