摘要
子空间聚类(Subspace clustering)是一种当前较为流行的基于谱聚类的高维数据聚类框架.近年来,由于深度神经网络能够有效地挖掘出数据深层特征,其研究倍受各国学者的关注.深度子空间聚类旨在通过深度网络学习原始数据的低维特征表示,计算出数据集的相似度矩阵,然后利用谱聚类获得数据的最终聚类结果.然而,现实数据存在维度过高、数据结构复杂等问题,如何获得更鲁棒的数据表示,改善聚类性能,仍是一个挑战.因此,本文提出基于自注意力对抗的深度子空间聚类算法(SAADSC).利用自注意力对抗网络在自动编码器的特征学习中施加一个先验分布约束,引导所学习的特征表示更具有鲁棒性,从而提高聚类精度.通过在多个数据集上的实验,结果表明本文算法在精确率(ACC)、标准互信息(NMI)等指标上都优于目前最好的方法.
Subspace clustering is a popular spectral clustering framework for high-dimensional data.In recent years,deep neural networks are witnessed to effectively mine the features of data,which has attracted the attention from various fields.Deep subspace clustering aims to learn the low-dimensional feature representation of data via the deep network,and subsequently calculate the similarity matrix fed into spectral clustering framework for final clustering result.However,real data are often with too high dimensions and complex data structure.How to obtain a robust data representation and improve clustering performance remains a challenge.Therefore,this paper proposes a deep subspace clustering algorithm(SAADSC)based on the self-attention adversarial mechanism.Specifically,the self-attention adversarial network is developed to impose a priori distribution constraint in the feature learning of the autoencoder to guide the learned feature representation to be more robust,thereby improving clustering accuracy.Through the experiments on multiple datasets,the results show that the proposed algorithm in this paper is superior to the state-of-the-art methods in terms of accuracy rate(ACC)and standard mutual information(NMI).
作者
尹明
吴浩杨
谢胜利
杨其宇
YIN Ming;WU Hao-Yang;XIE Sheng-Li;YANG Qi-Yu(School of Automation,Guangdong University of Technology,Guangzhou 510006)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第1期271-281,共11页
Acta Automatica Sinica
基金
国家自然科学基金(U1911401,61973087,61876042)
广东省自然科学基金(2020A1515011493)
流程工业综合自动化国家重点实验室开放课题基金项目(2020-KF-21-02)资助~。
关键词
子空间聚类
生成对抗网络
自注意力模型
深度学习
Subspace clustering
Generative adversarial networks
self-attention model
deep learning
