Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network
We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect t...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2743678 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564420362895360 |
|---|---|
| author | Xiangguang Dai Chuandong Li Biqun Xiang |
| author_facet | Xiangguang Dai Chuandong Li Biqun Xiang |
| author_sort | Xiangguang Dai |
| collection | DOAJ |
| description | We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method. |
| format | Article |
| id | doaj-art-a6293c508d94467b9bca35e331e5273e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a6293c508d94467b9bca35e331e5273e2025-02-03T01:11:06ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/27436782743678Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural NetworkXiangguang Dai0Chuandong Li1Biqun Xiang2College of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaCollege of Mobile Telecommunications, Chongqing University of Posts and Telecommunications, Chongqing 401520, ChinaWe present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method.http://dx.doi.org/10.1155/2018/2743678 |
| spellingShingle | Xiangguang Dai Chuandong Li Biqun Xiang Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network Complexity |
| title | Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network |
| title_full | Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network |
| title_fullStr | Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network |
| title_full_unstemmed | Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network |
| title_short | Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network |
| title_sort | graph sparse nonnegative matrix factorization algorithm based on the inertial projection neural network |
| url | http://dx.doi.org/10.1155/2018/2743678 |
| work_keys_str_mv | AT xiangguangdai graphsparsenonnegativematrixfactorizationalgorithmbasedontheinertialprojectionneuralnetwork AT chuandongli graphsparsenonnegativematrixfactorizationalgorithmbasedontheinertialprojectionneuralnetwork AT biqunxiang graphsparsenonnegativematrixfactorizationalgorithmbasedontheinertialprojectionneuralnetwork |