Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

TL;DR

This paper introduces GCAE, a novel multi-view binary clustering method that leverages auto-encoders and affinity graphs with low-rank constraints to improve clustering accuracy and efficiency.

Contribution

The paper proposes a unified auto-encoder based hashing framework that incorporates multi-view affinity graphs with low-rank constraints for enhanced clustering.

Findings

Outperforms state-of-the-art methods on 5 public datasets.

Effectively mines geometric information from multi-view data.

Reduces quantization errors through decorrelation and code balance constraints.

Abstract

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multi-view binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering (GCAE). Specifically, we propose a multi-view affinity graphs learning model with low-rank constraint, which can mine the underlying geometric information from multi-view data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we utilize an alternating iterative optimization scheme to obtain the multi-view clustering results. Extensive experimental results on $5$ public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.