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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摘要

大多数子空间聚类算法将高维数据映射到低维子空间时不能较好捕获数据间几何结构.针对上述问题,文中提出引入低秩约束先验的深度子空间聚类算法,兼顾数据全局和局部结构信息.算法结合低秩表示与深度自编码器,利用低秩约束捕获数据全局结构,并将约束神经网络的潜在特征表示为低秩.自编码通过最小化重构误差进行非线性低维子空间映射,保留数据的局部特性.以多元逻辑回归函数作为判别模型,预测子空间分割.整个算法在无监督联合学习框架下进行优化.在5个数据集上的实验验证文中方法的有效性.

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	张敏
	周治平

关键词 ：低秩约束先验, 自编码器, 归一化层, 联合学习框架

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

收稿日期: 2019-01-25

ZTFLH:

TP 18

作者简介: 张敏,硕士研究生,主要研究方向为聚类分析、模式识别.E-mail:m15061882373_1@163.com.周治平(通讯作者),博士,教授,主要研究方向为检测技术、自动化装置等.E-mail:zzp@jiangnan.edu.cn.

引用本文:

张敏,周治平. 引入低秩约束先验的深度子空间聚类[J]. 模式识别与人工智能, 2019, 32(7): 652-660. ZHANG Min, ZHOU Zhiping. Deep Subspace Clustering with Low Rank Constrained Prior. , 2019, 32(7): 652-660.

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

[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. Washington, USA: IEEE, 2015: 1582-1590.
[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. Palo Alto, USA: AAAI Press, 2016: 1925-1931.
[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, 2018, 355(8): 3795-3811.
[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, 2013, 35(1): 171-184.
[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. Washington, USA: IEEE, 2016. DOI: 10.1109/WHISPERS.2016.8071748.
[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, 2018, 29(10): 4882-4893.
[7] 谈超,关佶红,周水庚.增量与演化流形学习综述.智能系统学报, 2012, 7(5): 377-388.
(TAN C, GUAN J H, ZHOU S G. Incremental and Evolutionary Manifold Learning: A Survey. CAAI Transactions on Intelligent Systems, 2012, 7(5): 377-388.)
[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. DOI: https://doi.org/10.1007.
[9] DONG Y X, YANG C Z. Cluster-Based Least Absolute Deviation Regression for Dimension Reduction. Journal of Statistical Theory and Practice, 2016, 10(1): 121-132.
[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. Washington, USA: IEEE, 2017: 1965-1972.
[11] YANG B, FU X, SIDIROPOULOS N D, et al. Towards K-means-Friendly Spaces: Simultaneous Deep Learning and Clustering // Proc of the 34th International Conference on Machine Learning. New York, USA: ACM, 2017: 3861-3870.
[12] 黄健航,雷迎科.基于边际Fisher深度自编码器的电台指纹特征提取.模式识别与人工智能, 2017, 30(11): 1030-1038.
(HUANG J H, LEI Y K. Radio Fingerprint Extraction Based on Marginal Fisher Deep Autoencoder. Pattern Recognition and Artificial Intelligence, 2017, 30(11): 1030-1038.)
[13] CARON M, BOJANOWSKI P, JOULIN A, et al. Deep Clustering for Unsupervised Learning of Visual Features // Proc of the 15th European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 139-156.
[14] PENG X, FENG J S, XIAO S J, et al. Structured AutoEncoders for Subspace Clustering. IEEE Transactions on Image Processing, 2018, 27(10): 5076-5086.
[15] ZHAO J B, MATHIEU M, GOROSHIN R, et al. Stacked What-Where Auto-Encoders. Computer Science, 2015, 15(1): 3563-3593.
[16] DIZAJI K G, HERANDI A, DENG C, et al. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization // Proc of the 2th International Conference on Computer Vision. Washington, USA: IEEE, 2017: 5747-5756.
[17] YANG J W, PARIKH D, BATRA D. Joint Unsupervised Learning of Deep Representations and Image Clusters // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1354-1362.
[18] CHEN Y Y, ZHANG L, ZHANG Y. A Novel Low Rank Representation Algorithm for Subspace Clustering. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(4). DOI: 10.1142/S0218001416500075.
[19] 张涛,唐振民,吕建勇.一种基于低秩表示的子空间聚类改进算法.电子与信息学报, 2016, 38(11): 2811-2818.
(ZHANG T, TANG Z M, Lv J Y. Improved Algorithm Based on Low Rank Representation for Subspace Clustering. Journal of Electronics and Information Technology, 2016, 38(11): 2811-2818.)
[20] FAVARO P, VIDAL R, RAVICHANDRAN A. A Closed form Solution to Robust Subspace Estimation and Clustering // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2011: 1801-1807.
[21] GUO X F, GAO L, LIU X W, et al. Improved Deep Embedded Clustering with Local Structure Preservation // Proc of the 26th International Joint Conference on Artificial Intelligence. Washington, USA: IEEE, 2017: 1753-1759.
[22] ELHAMIFAR E, VIDAL R. Sparse Subspace Clustering: Algorithm, Theory, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2765-2781.
[23] TIAN F, GAO B, CUI Q, et al. Learning Deep Representations for Graph Clustering // Proc of the 28th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2014: 1293-1299.
[24] LU C Y, MIN H, ZHAO Z Q, et al. Robust and Efficient Subspace Segmentation via Least Squares Regression // Proc of the 12th European Conference on Computer Vision. Berlin, Germany: Springer, 2012: 347-360.