Agglomerative Neural Networks for Multi-view Clustering

TL;DR

This paper introduces Agglomerative Neural Networks (ANN) for multi-view clustering, which models view relationships more accurately and directly clusters data without postprocessing, showing promising results on multiple datasets.

Contribution

The paper proposes a novel agglomerative analysis approach with neural networks for multi-view clustering, extending to complex data scenarios and demonstrating robustness and effectiveness.

Findings

ANN outperforms state-of-the-art methods on four datasets.

ANN effectively models complex view relationships.

The approach avoids postprocessing like K-means.

Abstract

Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise comparison cannot portray the inter-view relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly while avoiding a dedicated postprocessing step (e.g., using K-means). We further extend ANN with learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures and extensibility in our case study and explain its robustness and effectiveness of data-driven modifications.