Deep Subspace Clustering with Low Rank Constrained Prior

被引：0
|
作者：
Zhang M. [1 ]
Zhou Z. [1 ,2 ]
机构：
[1] School of Internet of Things Engineering, Jiangnan University, Wuxi
[2] Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Jiangnan University, Wuxi
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 07期
关键词：
Autoencoder; Joint Learning Framework; Low Rank Constrained Prior; Soft-Max Layer;
D O I：
10.16451/j.cnki.issn1003-6059.201907009
中图分类号：
学科分类号：
摘要：
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. © 2019, Science Press. All right reserved.
引用
收藏
页码：652 / 660
页数：8
相关论文
共 24 条
  • [1] Zhang C.Q., Fu H.Z., Liu S., Et al., Low-Rank Tensor Constrained Multiview Subspace Clustering, Proc of the IEEE International Conference on Computer Vision, pp. 1582-1590, (2015)
  • [2] Peng X., Xiao S.J., Feng J.S., Et al., Deep Subspace Clustering with Sparsity Prior, Proc of the 25th International Joint Conference on Artificial Intelligence, pp. 1925-1931, (2016)
  • [3] Yin M., Wu Z.Z., Zeng D.Y., Et al., Sparse Subspace Clustering with Jointly Learning Representation and Affinity Matrix, Journal of the Franklin Institute, 355, 8, pp. 3795-3811, (2018)
  • [4] Liu G.C., Lin Z.C., Yan S.C., Et al., Robust Recovery of Subspace Structures by Low-Rank Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1, pp. 171-184, (2013)
  • [5] Zhang M.M., Li W., Du Q., Joint Low Rank and Sparse Representation-Based Hyperspectral Image Classification, Proc of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, (2016)
  • [6] Yao C., Han J.W., Nie F.P., Et al., Local Regression and Global Information-Embedded Dimension Reduction, IEEE Transactions on Neural Networks and Learning Systems, 29, 10, pp. 4882-4893, (2018)
  • [7] Tan C., Guan J.H., Zhou S.G., Incremental and Evolutionary Manifold Learning: A Survey, CAAI Transactions on Intelligent Systems, 7, 5, pp. 377-388, (2012)
  • [8] Tang K.W., Su Z.X., Jiang W., Et al., Robust Subspace Learning-Based Low-Rank Representation for Manifold Clustering, Neural Computing and Applications, (2018)
  • [9] Dong Y.X., Yang C.Z., Cluster-Based Least Absolute Deviation Regression for Dimension Reduction, Journal of Statistical Theory and Practice, 10, 1, pp. 121-132, (2016)
  • [10] Jiang Z., Zheng Y., Tan H.C., Et al., Variational Deep Embe-dding: A Generative Approach to Clustering, Proc of the 26th International Joint Conference on Artificial Intelligence, pp. 1965-1972, (2017)
123