引入低秩约束先验的深度子空间聚类

doi:10.16451/j.cnki.issn1003-6059.201907009

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Abstract

Most subspace clustering methods cannot capture geometric structures of data effetively while mapping high-dimensional data into a low-dimensional subspace. Aiming at this problem, a deep subspace clustering algorithm with low rank constrained prior(DSC-LRC) is proposed, maintaining both global and local structure information. Low-rank representation(LRR) is combined with depth autoencoder, global structures of data are captured by low rank constraint, and potential characteristics of constrained neural network are represented as low rank. Data are nonlinearly mapped into a latent space by minimizing differences between reconstructions and inputs with the local features of the data maintained. Multivariate logistic regression function is considered as a discriminant model to predict subspace segmentation. Parameters updating and clustering performance optimization are conducted in an unsupervised joint learning framework. Experiments on five datasets validate the effectiveness of DSC-LRC.

Key words： Low Rank Constrained Prior Autoencoder Soft-Max Layer Joint Learning Framework

Received: 25 January 2019

ZTFLH:

TP 18

About author:: ZHANG Min, master student. Her research interests include clustering analysis and pattern recognition.ZHOU Zhiping(Corresponding author), Ph.D., professor. His research interests include detection technology and automatic device.

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Cite this article:

ZHANG Min,ZHOU Zhiping. Deep Subspace Clustering with Low Rank Constrained Prior[J]. , 2019, 32(7): 652-660.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/652

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