Multi-view Subspace Clustering via Partition Fusion

TL;DR

This paper introduces a robust multi-view clustering method that fuses information in a partition space, improving performance by addressing noise and heterogeneity in multi-view data.

Contribution

It proposes a novel partition fusion approach that unifies graph learning, partition generation, and view weighting for enhanced robustness in multi-view clustering.

Findings

Outperforms existing methods on benchmark datasets.

Demonstrates increased robustness to noise and feature inconsistency.

Achieves higher clustering accuracy and stability.

Abstract

Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.