计算机科学
二进制代码
散列函数
聚类分析
理论计算机科学
编码器
人工智能
二进制数
模式识别(心理学)
算法
数学
计算机安全
算术
操作系统
作者
Huibing Wang,Mingze Yao,Guangqi Jiang,Zetian Mi,Xianping Fu
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
被引量:23
标识
DOI:10.1109/tnnls.2023.3239033
摘要
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 multiview 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 (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview 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 use an alternating iterative optimization scheme to obtain the multiview clustering results. 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.
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