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Efficient l1-norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method : Efficient l(1)-norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method
DC Field | Value | Language |
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dc.contributor.author | Kim, Eunwoo | - |
dc.contributor.author | Lee, Minsik | - |
dc.contributor.author | Choi, Chong-Ho | - |
dc.contributor.author | Kwak, Nojun | - |
dc.contributor.author | Oh, Songhwai | - |
dc.date.accessioned | 2024-08-08T01:40:54Z | - |
dc.date.available | 2024-08-08T01:40:54Z | - |
dc.date.created | 2018-09-27 | - |
dc.date.created | 2018-09-27 | - |
dc.date.issued | 2015-02 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, Vol.26 No.2, pp.237-251 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | https://hdl.handle.net/10371/207282 | - |
dc.description.abstract | Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l(2)-norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l(2)-norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l(1)-norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l(1)-norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods. | - |
dc.language | 영어 | - |
dc.publisher | IEEE Computational Intelligence Society | - |
dc.title | Efficient l1-norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method | - |
dc.title.alternative | Efficient l(1)-norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TNNLS.2014.2312535 | - |
dc.citation.journaltitle | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.identifier.wosid | 000348856200004 | - |
dc.identifier.scopusid | 2-s2.0-84921442961 | - |
dc.citation.endpage | 251 | - |
dc.citation.number | 2 | - |
dc.citation.startpage | 237 | - |
dc.citation.volume | 26 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Choi, Chong-Ho | - |
dc.contributor.affiliatedAuthor | Kwak, Nojun | - |
dc.contributor.affiliatedAuthor | Oh, Songhwai | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | L-1 NORM | - |
dc.subject.keywordAuthor | Alternating rectified gradient method | - |
dc.subject.keywordAuthor | l(1)-norm | - |
dc.subject.keywordAuthor | low-rank matrix approximation | - |
dc.subject.keywordAuthor | matrix completion (MC) | - |
dc.subject.keywordAuthor | principal component analysis (PCA) | - |
dc.subject.keywordAuthor | proximal gradient method | - |
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