Graph-based Multi-view Binary Learning for image clustering

Abstract

Hashing techniques, also known as binary code learning, have recently attracted 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 clustering tasks, most hashing research mainly mapped the original data into the Hamming space while heavily ignoring the original feature space structure. To solve 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). The binary code learning combines the graph structure of original data and the complementary information of different views for clustering. Specifically, GMBL preserves the local linear relationship utilizing the Laplacian matrix aim to maintain the graph-based structure of the original data in Hamming space. Moreover, by automatically assigning weights for each view to improve clustering performance, which takes distinctive contributions of multiple views into considerations. Besides, an alternating iterative optimization method is designed to solve the resulting optimization problems. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.