基于离散哈希的聚类  

Clustering based on discrete hashing

作　　者：轩书婷 刘惊雷 XUAN Shuting;LIU Jinglei(School of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005

出　　处：《计算机应用》2022年第3期713-723,共11页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62072391);山东省自然科学基金资助项目(ZR2020MF148)。

摘　　要：传统的聚类方法是在数据空间进行,且聚类数据的维度较高。为了解决这两个问题,提出了一种新的二进制图像聚类方法——基于离散哈希的聚类(CDH)。该框架通过L21范数实现自适应的特征选择,从而降低数据的维度;同时通过哈希方法将数据映射到二进制的汉明空间,随后,在汉明空间中对稀疏的二进制矩阵进行低秩矩阵分解,完成图像的快速聚类;最后使用可以快速收敛的优化方案来对目标函数进行优化求解。在Caltech101、Yale、COIL20、ORL图像数据集上的实验结果表明,该方法可以有效提升聚类效率。在Caltech101数据集的Gabor视图,与传统的K-means、谱聚类方法相比,在处理高维度数据时,CDH的时间效率分别提高了约87和98个百分点。The traditional clustering methods are carried out in the data space,and clustered data is high-dimensional.In order to solve these two problems,a new binary image clustering method,Clustering based on Discrete Hashing(CDH),was proposed.To reduce the dimension of data,L21-norm was used in this framework to realize adaptive feature selection.At the same time,the data was mapped into binary Hamming space by the hashing method.Then,the sparse binary matrix was decomposed into a low-rank matrix in the Hamming space to complete fast image clustering.Finally,an optimization scheme that could converge quickly was used to solve the objective function.Experimental results on image datasets(Caltech101,Yale,COIL20,ORL) show that this method can effectively improve the efficiency of clustering.Compared with the traditional clustering methods,such as K-means and Spectral Clustering(SC),the time efficiency of CDH was improved by87 and 98 percentage points respectively in the Gabor view of the Caltech101 dataset when processing high-dimensional data.

关 键 词：哈希方法 自动特征选择 稀疏二进制矩阵 L21范数 收敛优化 汉明空间 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]