Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework

TL;DR

This paper introduces a novel unified multi-view clustering framework that combines non-negative feature properties and multi-view information, improving clustering performance on benchmark datasets.

Contribution

It proposes the first unified model integrating multi-view data with non-negative features in a graph-based clustering framework.

Findings

Effective three-stage iterative solution derived.

Analytic solutions provided for sub-problems.

Demonstrated superior performance on benchmark datasets.

Abstract

Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of existing methods. In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC). Then, we derive an effective three-stage iterative solution for the proposed model and provide analytic solutions for the three sub-problems from the three stages. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features. Extensive experiments on three benchmark data sets demonstrate the effectiveness of the proposed method.