• 论文 •

### 基于流形正则化与成对约束的深度半监督谱聚类算法

1. 1. 辽宁工程技术大学软件学院,葫芦岛 125105; 2. 西北工业大学计算机科学学院,西安 710072; 3. 中国科学院海西研究院泉州装备制造研究所,泉州 362216
• 出版日期:2020-08-25 发布日期:2020-09-24

XIAO Chenglong, ZHANG Zhongpeng, WANG Shanshan, ZHANG Rui,WANWanli, WI Xian. Deep Semi-Supervised Spectral Clustering Algorithm Based on Regularization of Manifold and Pairwise Constraints[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(8): 1325-1341.

### Deep Semi-Supervised Spectral Clustering Algorithm Based on Regularization of Manifold and Pairwise Constraints

XIAO Chenglong1 ,ZHANG Zhongpeng1 ,WANG Shanshan1 ,ZHANG Rui2,WANWanli3 ,WI Xian3

1. 1. School of Software, Liaoning Technical University, Huludao 125105; 2. School of Computer Science, Northwestern Polytechnical University,Xi’an 710072; 3. Quanzhou Institute of Equipment Manufacturing Haixi Institutes, Chinese Academy of Sciences, Quanzhou 362216
• Online:2020-08-25 Published:2020-09-24

Existing subspace clustering methods rest on a global linear data set, which employs prior constraints to estimate underlying subspace of unlabeled data points and clusters them into corresponding groups, thus may fail in handing data with nonlinear structure. Motivated by the huge success achieved by deep learning with its powerful nonlinear representation ability in many applications, in this paper we propose a novel deep simi-supervised spectral clustering approach through joint learning with autoencoder (MPAE), which incorporates regularization of manifold learning and pairwise constraints into the structure of data representation and exploits the $k$-nearest neighbors constraint to construct the similarity matrix. On the one hand, This method preserves the reconstruction and global features of local manifold structure of the data simultaneously, and on the other hand, the pair constraint rules among known samples are integrated into the target optimization design, which makes the learned low-dimensional features more discriminant and improves the clustering performance of the algorithm. Finally, the related clustering algorithm is adopted for clustering. Extensive experiments on several datasets for subspace clustering were conducted. They demonstrated that the proposed algorithm achieves better clustering results.

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