DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Qiaosheng | ko |
dc.contributor.author | Suh, Geewon | ko |
dc.contributor.author | Suh, Changho | ko |
dc.contributor.author | Tan, Vincent Y. F. | ko |
dc.date.accessioned | 2022-06-28T02:01:40Z | - |
dc.date.available | 2022-06-28T02:01:40Z | - |
dc.date.created | 2022-06-27 | - |
dc.date.created | 2022-06-27 | - |
dc.date.created | 2022-06-27 | - |
dc.date.created | 2022-06-27 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON SIGNAL PROCESSING, v.70, pp.2681 - 2697 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.uri | http://hdl.handle.net/10203/297138 | - |
dc.description.abstract | In this paper, we design and analyze Mc2g (Matrix Completion with 2 Graphs), an efficient algorithm that performs matrix completion in the presence of social and item similarity graphs. Mc2g runs in quasilinear time and is parameter free. It is based on spectral clustering and local refinement steps. For the matrix completion problem which possesses additional block structures in its rows and columns, we derive the expected number of sampled entries required for Mc2g to succeed, and further show that it matches an information-theoretic lower bound up to a constant factor for a wide range of parameters. We perform extensive experiments on both synthetic datasets and a semi-real dataset inspired by real graphs. The experimental results show that Mc2g outperforms other state-of-the-art matrix completion algorithms. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | MC2G: An Efficient Algorithm for Matrix Completion With Social and Item Similarity Graphs | - |
dc.type | Article | - |
dc.identifier.wosid | 000809391700005 | - |
dc.identifier.scopusid | 2-s2.0-85132244832 | - |
dc.type.rims | ART | - |
dc.citation.volume | 70 | - |
dc.citation.beginningpage | 2681 | - |
dc.citation.endingpage | 2697 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON SIGNAL PROCESSING | - |
dc.identifier.doi | 10.1109/TSP.2022.3174423 | - |
dc.contributor.localauthor | Suh, Changho | - |
dc.contributor.nonIdAuthor | Zhang, Qiaosheng | - |
dc.contributor.nonIdAuthor | Tan, Vincent Y. F. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Clustering algorithms | - |
dc.subject.keywordAuthor | Signal processing algorithms | - |
dc.subject.keywordAuthor | Computational efficiency | - |
dc.subject.keywordAuthor | Social networking (online) | - |
dc.subject.keywordAuthor | Recommender systems | - |
dc.subject.keywordAuthor | Maximum likelihood estimation | - |
dc.subject.keywordAuthor | Prediction algorithms | - |
dc.subject.keywordAuthor | Matrix completion | - |
dc.subject.keywordAuthor | community detection | - |
dc.subject.keywordAuthor | stochastic block model | - |
dc.subject.keywordAuthor | graph side information | - |
dc.subject.keywordPlus | COMMUNITY DETECTION | - |
dc.subject.keywordPlus | EXACT RECOVERY | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.