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  2019, Vol. 32 Issue (7): 652-660    DOI: 10.16451/j.cnki.issn1003-6059.201907009
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Deep Subspace Clustering with Low Rank Constrained Prior
ZHANG Min, ZHOU Zhiping
1.School of Internet of Things Engineering, Jiangnan University, Wuxi 214122
2.Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi 214122

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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 wordsLow 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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ZHANG Min
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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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