基于自注意力对抗的深度子空间聚类

尹明; 吴浩杨; 谢胜利; 杨其宇

doi:10.16383/j.aas.c200302

基于自注意力对抗的深度子空间聚类

doi: 10.16383/j.aas.c200302

1.
广东工业大学自动化学院广州 510006

基金项目: 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

详细信息

作者简介:
尹明：广东工业大学自动化学院教授. 主要研究方向为图像处理, 模式识别, 计算机视觉, 机器学习. E-mail: yiming@gdut.edu.cn

吴浩杨：广东工业大学自动化学院硕士研究生.主要研究方向为子空间学习, 深度聚类. E-mail: tarkovskyfans@163.com

谢胜利：广东工业大学自动化学院教授, IEEE Fellow. 主要研究方向为盲信号处理, 生物医学信号处理. E-mail: shlxie@gdut.edu.cn

杨其宇：广东工业大学自动化学院讲师. 主要研究方向为信号处理, 实时数据处理. 本文通信作者 E-mail: yangqiyu@gdut.edu.cn

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出版历程
- 收稿日期: 2020-05-12
- 网络出版日期: 2022-01-25
- 刊出日期: 2022-01-25

Self-attention Adversarial Based Deep Subspace Clustering

1.
School of Automation, Guangdong University of Technology, Guangzhou 510006

Funds: Supported by National Natural Science Foundation of China (U1911401, 61973087, 61876042), Guangdong Basic and Applied Basic Research Foundation (2020A1515011493), and State Key Laboratory of Synthetical Automation for Process Industries (2020-KF-21-02)

More Information

Author Bio:
YIN Ming　Professor at the School of Automation, Guangdong University of Technology. His research interest covers image processing, pattern recognition, computer vision, and machine learning

WU Hao-Yang　Master student at the School of Automation, Guangdong University of Technology. His research interest covers subspace learning and deep clustering

XIE Sheng-Li　Professor at the School of Automation, Guangdong University of Technology, IEEE Fellow. His research interest covers blind signal processing, and biomedical signal processing

YANG Qi-Yu　Lecturer at the School of Automation, Guangdong University of Technology. His research interest covers signal processing and real time data processing. Corresponding author of this paper

摘要

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.
- 子空间聚类 /
- 生成对抗网络 /
- 自注意力模型 /
- 深度学习
Abstract: 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).
- Subspace clustering /
- Generative adversarial networks /
- self-attention model /
- deep learning

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图 1 深度子空间聚类网络结构图

Fig. 1 The framework of Deep Subspace Clustering

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图 2 生成对抗网络结构图

Fig. 2 The framework of generative adversarial networks

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图 4 基于自注意力对抗的深度子空间聚类网络框架

Fig. 4 The framework of self-attention adversarial network based deep subspace clustering

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图 3 自注意力模块

Fig. 3 Self-attention module

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图 5 MNIST的网络训练损失

Fig. 5 The loss function of SAADSC during training on MNIST

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表 1 数据集信息

Table 1 Information of the datasets

数据集	类别	数量	大小
MNIST	10	1000	28×28
FMNIST	10	1000	28×28
COIL-20	20	1440	32×32
YaleB	38	2432	48×32
USPS	10	9298	16×16

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表 2 参数设置

Table 2 Parameter setting

数据集	$\lambda _1$	$\lambda _2$	$\lambda _3$
MNIST	1	0.5	10
FMNIST	1	0.0001	100
COIL-20	1	30	10
YaleB	1	0.06	24
USPS	1	0.1	10

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表 3 网络结构参数

Table 3 Network structure parameter

数据集	卷积核大小	通道数
MNIST	[5, 3, 3]	[10, 20, 30]
FMNIST	[5, 3, 3, 3]	[10, 20, 30, 40]
COIL-20	[3]	[15]
YaleB	[5, 3, 3]	[64, 128, 256]
USPS	[5, 3, 3]	[10, 20, 30]

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表 4 5个数据集的实验结果

Table 4 Experimental results of five datasets

数据集	YaleB		COIL-20		MNIST		FMNIST		USPS
度量方法	ACC	NMI	ACC	NMI	ACC	NMI	ACC	NMI	ACC	NMI
DSC-L1	0.9667	0.9687	0.9314	0.9395	0.7280	0.7217	0.5769	0.6151	0.6984	0.6765
DSC-L2	0.9733	0.9703	0.9368	0.9408	0.7500	0.7319	0.5814	0.6133	0.7288	0.6963
DEC	*	*	0.6284	0.7789	0.8430	0.8000	0.5900	0.6010	0.7529	0.7408
DCN	0.4300	0.6300	0.1889	0.3039	0.7500	0.7487	0.5867	0.5940	0.7380	0.7691
StructAE	0.9720	0.9734	0.9327	0.9566	0.6570	0.6898	−	−	−	−
DASC	0.9856	0.9801	0.9639	0.9686	0.8040	0.7800	−	−	−	−
SAADSC	0.9897	0.9856	0.9750	0.9745	0.9540	0.9281	0.6318	0.6246	0.7850	0.8134

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表 5 不同先验分布的实验结果

Table 5 Clustering results on different prior distributions

数据集	MNIST		FMNIST		USPS
度量方法	ACC	NMI	ACC	NMI	ACC	NMI
高斯分布	0.9540	0.9281	0.6318	0.6246	0.7850	0.8134
伯努利分布	0.9320	0.9043	0.6080	0.5990	0.7755	0.7917
确定性分布	0.8670	0.8362	0.5580	0.5790	0.7796	0.7914

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表 6 SAADSC网络中不同模块的作用

Table 6 Ablation study on SAADSC

数据集	YaleB		COIL-20		MNIST		FMNIST		USPS
度量方法	ACC	NMI	ACC	NMI	ACC	NMI	ACC	NMI	ACC	NMI
Test1	0.9725	0.9672	0.9382	0.9493	0.8820	0.8604	0.6080	0.6110	0.7748	0.7838
Test2	0.0711	0.0961	0.4229	0.6263	0.6420	0.5940	0.5380	0.4917	0.6105	0.5510
Test3	0.0843	0.1222	0.6993	0.7855	0.6610	0.6763	0.6140	0.5922	0.3826	0.3851
Test4	0.9782	0.9702	0.9683	0.9741	0.9500	0.9275	0.6211	0.6143	0.7850	0.7986
DSC-L2	0.9733	0.9703	0.9368	0.9408	0.7500	0.7319	0.5814	0.6133	0.7288	0.6963
SAADSC	0.9897	0.9856	0.9750	0.9745	0.9540	0.9281	0.6318	0.6246	0.7850	0.8134

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表 7 含有噪声的COIL-20聚类结果

Table 7 Clustering results on the noisy COIL-20

算法	SAADSC		DSC-L1		DSC-L2		DASC
度量方法	ACC	NMI	ACC	NMI	ACC	NMI	ACC	NMI
无噪声	0.9750	0.9745	0.9314	0.9353	0.9368	0.9408	0.9639	0.9686
10%噪声	0.9590	0.9706	0.8751	0.8976	0.8714	0.9107	0.9021	0.9392
20%噪声	0.9111	0.9593	0.8179	0.8736	0.8286	0.8857	0.8607	0.9193
30%噪声	0.8708	0.9638	0.7989	0.8571	0.8072	0.8784	0.8357	0.9143
40%噪声	0.8569	0.9272	0.6786	0.7857	0.7250	0.8187	0.7805	0.8753

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表 8 含有噪声的USPS聚类结果

Table 8 Clustering results on the noisy USPS

算法	SAADSC		DSC-L1		DSC-L2
度量方法	ACC	NMI	ACC	NMI	ACC	NMI
无噪声	0.7850	0.8134	0.6984	0.6765	0.7288	0.6963
10%噪声	0.7778	0.7971	0.6704	0.6428	0.6562	0.6628
20%噪声	0.7757	0.7901	0.6667	0.6158	0.6530	0.6429
30%噪声	0.7719	0.7844	0.6386	0.5987	0.6454	0.6394
40%噪声	0.7674	0.7750	0.6042	0.5752	0.6351	0.6164

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