Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval
Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9258904/ |
| _version_ | 1818579371036770304 |
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| author | Jiang Xiong Yingyin Tao Meng Zhang Huaqing Li |
| author_facet | Jiang Xiong Yingyin Tao Meng Zhang Huaqing Li |
| author_sort | Jiang Xiong |
| collection | DOAJ |
| description | Traditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this paper, we construct an affinity graph to encode the geometrical structure of the original data, and the learned binary code subspace achieved by matrix factorization respects the structure. The proposed problem can be formulated as a mixed integer optimization problem. Therefore, we transform it into several sub-problems including an integer optimization problem, two convex problems with the non-negative constraint and a quadratic programming problem. Optimizing each sub-problem alternately until we achieve a local optimal solution. Image clustering and retrieval on image datasets show the excellent performance of our method in comparison to other dimensional reduction methods. |
| first_indexed | 2024-12-16T07:00:38Z |
| format | Article |
| id | doaj.art-996ac2b15f2d4e8a856287861b549ab9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T07:00:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-996ac2b15f2d4e8a856287861b549ab92022-12-21T22:40:10ZengIEEEIEEE Access2169-35362020-01-01820701220702310.1109/ACCESS.2020.30379569258904Binary Codes Based on Non-Negative Matrix Factorization for Clustering and RetrievalJiang Xiong0https://orcid.org/0000-0003-0158-1399Yingyin Tao1https://orcid.org/0000-0001-8441-1357Meng Zhang2Huaqing Li3https://orcid.org/0000-0001-6310-8965School of Three Gorges Artificial Intelligence, Chongqing Three Gorges University, Chongqing, ChinaChongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, ChinaKey Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaTraditional non-negative matrix factorization methods cannot learn the subspace from the high-dimensional data space composed of binary codes. One hopes to discover a compact parts-based representation composed of binary codes, which can uncover the intrinsic information and simultaneously respect the geometric structure of the original data. For this purpose, we introduce discrete hashing methods and propose a novel non-negative matrix factorization to generate binary codes from the original data. In this paper, we construct an affinity graph to encode the geometrical structure of the original data, and the learned binary code subspace achieved by matrix factorization respects the structure. The proposed problem can be formulated as a mixed integer optimization problem. Therefore, we transform it into several sub-problems including an integer optimization problem, two convex problems with the non-negative constraint and a quadratic programming problem. Optimizing each sub-problem alternately until we achieve a local optimal solution. Image clustering and retrieval on image datasets show the excellent performance of our method in comparison to other dimensional reduction methods.https://ieeexplore.ieee.org/document/9258904/Binary codesdiscrete hashingnon-negative matrix factorizationdimensional reduction |
| spellingShingle | Jiang Xiong Yingyin Tao Meng Zhang Huaqing Li Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval IEEE Access Binary codes discrete hashing non-negative matrix factorization dimensional reduction |
| title | Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval |
| title_full | Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval |
| title_fullStr | Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval |
| title_full_unstemmed | Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval |
| title_short | Binary Codes Based on Non-Negative Matrix Factorization for Clustering and Retrieval |
| title_sort | binary codes based on non negative matrix factorization for clustering and retrieval |
| topic | Binary codes discrete hashing non-negative matrix factorization dimensional reduction |
| url | https://ieeexplore.ieee.org/document/9258904/ |
| work_keys_str_mv | AT jiangxiong binarycodesbasedonnonnegativematrixfactorizationforclusteringandretrieval AT yingyintao binarycodesbasedonnonnegativematrixfactorizationforclusteringandretrieval AT mengzhang binarycodesbasedonnonnegativematrixfactorizationforclusteringandretrieval AT huaqingli binarycodesbasedonnonnegativematrixfactorizationforclusteringandretrieval |