| Presentation | 2017-05-25 Graph Learning for Spectral Clustering using Low-rank and Sparse Decomposition Taiju Kanada, Masaki Onuki, Yuichi Tanaka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Spectral clustering is a method of clustering using eigenvectors of graph Laplacian. By using appropriate graphs, it is known that spectral clustering shows superior results compared to other clustering methods such as the k-means method. That is, the performance of spectral clustering strongly depends on the graph. In this report, we propose a method to create a refined graph by low-rank/sparse decomposition of the adjacency matrix in order to improve the performance of spectral clustering. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Graph learning / low-rank sparse decomposition / ADMM / spectral clustering |
| Paper # | SIP2017-10,IE2017-10,PRMU2017-10,MI2017-10 |
| Date of Issue | 2017-05-18 (SIP, IE, PRMU, MI) |
| Conference Information | |
| Committee | PRMU / IE / MI / SIP |
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| Conference Date | 2017/5/25(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Eisaku Maeda(NTT) / Seishi Takamura(NTT) / Yoshitaka Masutani(Hiroshima City Univ.) / Makoto Nakashizuka(Chiba Inst. of Tech.) |
| Vice Chair | Seiichi Uchida(Kyushu Univ.) / Hironobu Fujiyoshi(Chubu Univ.) / Takayuki Hamamoto(Tokyo Univ. of Science) / Atsuro Ichigaya(NHK) / Yoshiki Kawata(Tokushima Univ.) / Yuichi Kimura(Kinki Univ.) / Masahiro Okuda(Univ. of Kitakyushu) / Shogo Muramatsu(Niigata Univ.) |
| Secretary | Seiichi Uchida(Kyoto Univ.) / Hironobu Fujiyoshi(NTT) / Takayuki Hamamoto(NTT) / Atsuro Ichigaya(Chiba Inst. of Tech.) / Yoshiki Kawata(Aichi Inst. of Tech.) / Yuichi Kimura(Nagoya Inst. of Tech.) / Masahiro Okuda(Ritsumeikan Univ.) / Shogo Muramatsu(Chiba Inst. of Tech.) |
| Assistant | Masaki Oonishi(AIST) / Takuya Funatomi(NAIST) / Kei Kawamura(KDDI R&D Labs.) / Keita Takahashi(Nagoya Univ.) / Ryo Haraguchi(Univ. of Hyogo) / Yasushi Hirano(Yamaguchi Univ.) / Osamu Watanabe(Takushoku Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Technical Committee on Image Engineering / Technical Committee on Medical Imaging / Technical Committee on Signal Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Graph Learning for Spectral Clustering using Low-rank and Sparse Decomposition |
| Sub Title (in English) | |
| Keyword(1) | Graph learning |
| Keyword(2) | low-rank sparse decomposition |
| Keyword(3) | ADMM |
| Keyword(4) | spectral clustering |
| 1st Author's Name | Taiju Kanada |
| 1st Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 2nd Author's Name | Masaki Onuki |
| 2nd Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 3rd Author's Name | Yuichi Tanaka |
| 3rd Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| Date | 2017-05-25 |
| Paper # | SIP2017-10,IE2017-10,PRMU2017-10,MI2017-10 |
| Volume (vol) | vol.117 |
| Number (no) | SIP-47,IE-48,PRMU-49,MI-50 |
| Page | pp.pp.55-60(SIP), pp.55-60(IE), pp.55-60(PRMU), pp.55-60(MI), |
| #Pages | 6 |
| Date of Issue | 2017-05-18 (SIP, IE, PRMU, MI) |