Graph-based Multi-view Binary Learning for Image Clustering

TL;DR

This paper introduces GMBL, a graph-based multi-view binary learning algorithm that encodes multi-view data into compact binary codes for improved clustering, preserving data structure and automatically weighting views.

Contribution

The paper proposes a novel multi-view binary clustering method that preserves data structure using graph embedding and directly optimizes binary codes without relaxation.

Findings

Outperforms previous methods on five datasets

Effectively preserves local data structure

Automatically weights multiple views for better clustering

Abstract

Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant prior researches mainly focus on learning discrete hash code while few works take original data structure into consideration. To address these problems, we propose a novel binary code algorithm for clustering, which adopts graph embedding to preserve the original data structure, called (Graph-based Multi-view Binary Learning) GMBL in this paper. GMBL mainly focuses on encoding the information of multiple views into a compact binary code, which explores complementary information from multiple views. In particular, in order to maintain the graph-based structure of the original data, we adopt a Laplacian matrix to preserve the local linear relationship of the data and map it to the Hamming space. Considering different views have distinctive contributions to the final clustering results, GMBL adopts a strategy of automatically assign weights for each view to better guide the clustering. Finally, An alternating iterative optimization method is adopted to optimize discrete binary codes directly instead of relaxing the binary constraint in two steps. Experiments on five public datasets demonstrate the superiority of our proposed method compared with previous approaches in terms of clustering performance.