Deep spectral clustering algorithm with L1 regularization

doi:10.11772/j.issn.1001-9081.2022121822

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (12): 3662-3667.DOI: 10.11772/j.issn.1001-9081.2022121822

• Artificial intelligence • Previous Articles Next Articles

Deep spectral clustering algorithm with L1 regularization

Wenbo LI¹^,², Bo LIU¹^,²(), Lingling TAO¹^,², Fen LUO¹^,², Hang ZHANG¹^,²

^1.School of Artificial Intelligence，Chongqing Technology and Business University，Chongqing 400067，China
^2.Chongqing Key Laboratory of Intelligent Perception and Block Chain Technology （Chongqing Technology and Business University），Chongqing 400067，China

Received:2022-12-06 Revised:2023-02-20 Accepted:2023-02-27 Online:2023-03-03 Published:2023-12-10
Contact: Bo LIU
About author:LI Wenbo， born in 1998， M. S. candidate. His research interests include deep clustering， unsupervised learning， computer vision.
TAO Lingling， born in 1998， M. S. candidate. Her research interests include computer vision， image processing， generative adversarial network.
LUO Fen， born in 1975， M. S.，lecturer. His research interests include computer vision， medical image processing.
ZHANG Hang， born in 1998， M. S. candidate. His research interests include machine learning， computer vision， deep clustering.
Supported by:
Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202200803);Chongqing Natural Science Foundation(cstc2018jcyjAX0057);Graduate Innovative Scientific Research Project of Chongqing Technology and Business University(yjscxx2022-112-68)

L1正则化的深度谱聚类算法

李文博¹^,², 刘波¹^,²(), 陶玲玲¹^,², 罗棻¹^,², 张航¹^,²

^1.重庆工商大学人工智能学院，重庆 400067
^2.智能感知与区块链技术重庆市重点实验室（重庆工商大学），重庆 400067

通讯作者: 刘波
作者简介:李文博（1998—），男，重庆人，硕士研究生，主要研究方向：深度聚类、无监督学习、计算机视觉
陶玲玲（1998—），女，重庆人，硕士研究生，主要研究方向：计算机视觉、图像处理、生成对抗网络
罗棻（1975—），男，重庆人，讲师，硕士，主要研究方向：计算机视觉、医学图像处理
张航（1998—），男，重庆人，硕士研究生，主要研究方向：机器学习、计算机视觉、深度聚类。
基金资助:
重庆市教委科学技术研究项目(KJZD?K202200803);重庆市自然科学基金资助项目(cstc2018jcyjAX0057);重庆工商大学研究生“创新型科研项目”(yjscxx2022?112?68)

Abstract

Abstract:

Aiming at the problems that the deep spectral clustering models perform poorly in training stability and generalization capability， a Deep Spectral Clustering algorithm with L1 Regularization （DSCLR） was proposed. Firstly， L1 regularization was introduced into the objective function of deep spectral clustering to sparsify the eigen vectors of the Laplacian matrix generated by the deep neural network model. And the generalization capability of the model was enhanced. Secondly， the network structure of the spectral clustering algorithm based on deep neural network was improved by using the Parametric Rectified Linear Unit activation function （PReLU） to solve the problems of model training instability and underfitting. Experimental results on MNIST dataset show that the proposed algorithm improves Clustering Accuracy （CA）， Normalized Mutual Information （NMI） index， and Adjusted Rand Index （ARI） by 11.85， 7.75， and 17.19 percentage points compared to the deep spectral clustering algorithm， respectively. Furthermore， the proposed algorithm also significantly improves the three evaluation metrics， CA， NMI and ARI， compared to algorithms such as Deep Embedded Clustering （DEC） and Deep Spectral Clustering using Dual Autoencoder Network （DSCDAN）.

Key words: deep clustering, spectral clustering, L1 regularization, deep learning, unsupervised learning

摘要：

针对深度谱聚类模型训练不稳定和泛化能力弱等问题，提出L1正则化的深度谱聚类算法（DSCLR）。首先，在深度谱聚类的目标函数中引入L1正则化，使深度神经网络模型生成的拉普拉斯矩阵的特征向量稀疏化，并提升模型的泛化能力；其次，通过利用参数化修正线性单元激活函数（PReLU）改进基于深度神经网络的谱聚类算法的网络结构，解决模型训练不稳定和欠拟合问题。在MNIST数据集上的实验结果表明，所提算法在聚类精度（CA）、归一化互信息（NMI）指数和调整兰德系数（ARI）这3个评价指标上，相较于深度谱聚类算法分别提升了11.85、7.75和17.19个百分点。此外，所提算法相较于深度嵌入聚类（DEC）和基于对偶自编码器网络的深度谱聚类（DSCDAN）等算法，在CA、NMI和ARI这3个评价指标上也有大幅提升。

关键词: 深度聚类, 谱聚类, L1正则化, 深度学习, 无监督学习

CLC Number:

TP311.5

Wenbo LI, Bo LIU, Lingling TAO, Fen LUO, Hang ZHANG. Deep spectral clustering algorithm with L1 regularization[J]. Journal of Computer Applications, 2023, 43(12): 3662-3667.

李文博, 刘波, 陶玲玲, 罗棻, 张航. L1正则化的深度谱聚类算法[J]. 《计算机应用》唯一官方网站, 2023, 43(12): 3662-3667.

Figures/Tables 8

Fig.1 Overall framework of DSCLR model

Tab.1 Network architecture of DSCLR

模块	网络结构
模块	全连接层维度	激活函数
自编码器模块	$[d, 1 024]$	ReLU
	$[1 024,512]$	ReLU
	$[512, k]$	—
特征映射模块	$[k, 1 024]$	ReLU
	$[1 024,2 048]$	ReLU
	$[2 048,1 024]$	ReLU
	$[2 048,1 024]$	ReLU
	$[512, k]$	PReLU

Tab.1 Network architecture of DSCLR

模块	网络结构
模块	全连接层维度	激活函数
自编码器模块	$[d, 1 024]$	ReLU
	$[1 024,512]$	ReLU
	$[512, k]$	—
特征映射模块	$[k, 1 024]$	ReLU
	$[1 024,2 048]$	ReLU
	$[2 048,1 024]$	ReLU
	$[2 048,1 024]$	ReLU
	$[512, k]$	PReLU

Fig. 2 Some images in COIL20 dataset

Tab.2 Dataset details

数据集	样本数	类别数	数据维度
MNIST	70 000	10	28×28×1
USPS	9 298	10	28×28×1
FASHION	70 000	10	28×28×1
DIGITS	28 000	10	28×28×1
COIL20	1 440	20	128×128×1

Tab.3 CA，NMI and ARI values of different algorithms on different datasets

算法	MNIST			DIGITS			USPS			COIL20			FASHION
算法	CA	NMI	ARI	CA	NMI	ARI	CA	NMI	ARI	CA	NMI	ARI	CA	NMI	ARI
k-means	50.14	54.67	38.10	49.44	58.67	38.83	56.61	61.39	46.33	76.16	62.85	50.91	51.46	48.3	34.98
AC	71.14	69.48	60.63	66.44	63.65	52.37	61.69	57.30	44.74	80.95	68.06	57.46	52.59	51.31	34.78
DBSCAN	23.56	20.55	4.22	24.34	19.70	4.34	8.53	20.58	1.78	3.17	8.33	0.13	25.11	20.95	2.47
SC	66.71	60.23	49.32	72.45	72.09	58.57	67.79	58.94	51.89	79.11	62.15	56.92	57.85	51.73	38.51
DEC	84.40	81.60	79.50	78.40	80.05	76.33	61.90	58.60	55.40	61.00	62.10	55.32	57.81	62.83	45.71
SpectralNet	73.75	72.17	63.63	84.11	87.91	82.47	63.53	60.65	53.03	50.47	46.95	49.20	58.41	66.06	46.30
SCDE	83.31	79.02	81.31	86.79	85.91	84.19	64.67	67.04	56.97	64.56	67.86	54.56	49.29	66.45	49.29
DSCDAN	77.60	74.50	78.34	85.40	80.10	86.25	69.80	65.20	57.56	47.60	64.20	53.64	54.60	55.60	40.82
DSCLR	85.60	79.92	80.82	90.97	88.50	87.13	65.97	61.39	53.16	69.44	70.26	61.25	65.88	66.82	46.72

Fig.3 t-SNE visualization results on MNIST dataset

Fig.4 Influence of β value on evaluation indexes on MNIST dataset

Fig.5 Influence of different training strategies on algorithm performance

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Deep spectral clustering algorithm with L1 regularization

L1正则化的深度谱聚类算法

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