Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification
Nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data. Due to the fact that it introduces the label information, semi-supervised NMF h...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8408457/ |
| _version_ | 1818924449620033536 |
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| author | Wenjie Zhu Yunhui Yan |
| author_facet | Wenjie Zhu Yunhui Yan |
| author_sort | Wenjie Zhu |
| collection | DOAJ |
| description | Nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data. Due to the fact that it introduces the label information, semi-supervised NMF has been demonstrated more advantageous in image representation than original NMF. However, previous semi-supervised NMF variants construct a label indicator matrix only for tagging the labeled data and not being optimized together with the matrix factorization. It is short of label propagation and fails to work for predicting the attribution of data. Moreover, the transductive semi-supervised NMF variants cannot dispose the prediction of unseen data, restricting the application of NMF. In this paper, ajoint optimization framework of linear regression and NMF (LR-NMF) based on the self-organized graph is proposed for a completed task which simultaneously takes into account image representation and attribution prediction. By minimizing the proposed objective, three interactive threads are running: decomposing the data into nonnegative basis matrix and the corresponding representation, linear regression using the nonnegative representation, and label propagation based on the self-organized graph which is defined in the feature space. The products of LR-NMF can be viewed as extracting nonnegative feature for clustering, meanwhile, they can be used to solve the out-of-sample problem for classification. Extensive clustering and classification experiments on the digit, face, and object challenging data sets are presented to show the efficacy of the proposed LR-NMF algorithm. |
| first_indexed | 2024-12-20T02:25:31Z |
| format | Article |
| id | doaj.art-46a6320875704a42b851a63828eb7169 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:25:31Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-46a6320875704a42b851a63828eb71692022-12-21T19:56:43ZengIEEEIEEE Access2169-35362018-01-016388203883410.1109/ACCESS.2018.28542328408457Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and ClassificationWenjie Zhu0https://orcid.org/0000-0001-7365-3481Yunhui Yan1School of Mechanical Engineering and Automation, Northeastern University, Shenyang, ChinaSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang, ChinaNonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data. Due to the fact that it introduces the label information, semi-supervised NMF has been demonstrated more advantageous in image representation than original NMF. However, previous semi-supervised NMF variants construct a label indicator matrix only for tagging the labeled data and not being optimized together with the matrix factorization. It is short of label propagation and fails to work for predicting the attribution of data. Moreover, the transductive semi-supervised NMF variants cannot dispose the prediction of unseen data, restricting the application of NMF. In this paper, ajoint optimization framework of linear regression and NMF (LR-NMF) based on the self-organized graph is proposed for a completed task which simultaneously takes into account image representation and attribution prediction. By minimizing the proposed objective, three interactive threads are running: decomposing the data into nonnegative basis matrix and the corresponding representation, linear regression using the nonnegative representation, and label propagation based on the self-organized graph which is defined in the feature space. The products of LR-NMF can be viewed as extracting nonnegative feature for clustering, meanwhile, they can be used to solve the out-of-sample problem for classification. Extensive clustering and classification experiments on the digit, face, and object challenging data sets are presented to show the efficacy of the proposed LR-NMF algorithm.https://ieeexplore.ieee.org/document/8408457/Nonnegative matrix factorizationlinear regressionself-organized graphsemi-supervised clustering and classification |
| spellingShingle | Wenjie Zhu Yunhui Yan Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification IEEE Access Nonnegative matrix factorization linear regression self-organized graph semi-supervised clustering and classification |
| title | Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification |
| title_full | Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification |
| title_fullStr | Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification |
| title_full_unstemmed | Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification |
| title_short | Joint Linear Regression and Nonnegative Matrix Factorization Based on Self-Organized Graph for Image Clustering and Classification |
| title_sort | joint linear regression and nonnegative matrix factorization based on self organized graph for image clustering and classification |
| topic | Nonnegative matrix factorization linear regression self-organized graph semi-supervised clustering and classification |
| url | https://ieeexplore.ieee.org/document/8408457/ |
| work_keys_str_mv | AT wenjiezhu jointlinearregressionandnonnegativematrixfactorizationbasedonselforganizedgraphforimageclusteringandclassification AT yunhuiyan jointlinearregressionandnonnegativematrixfactorizationbasedonselforganizedgraphforimageclusteringandclassification |