| Presentation | 2018-06-13 A supervised dimensionality reduction method using linear combinations of multiple eigenvectors Akira Imakura, Momo Matsuda, Tetsuya Sakurai, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Dimensionality reduction methods that reduce the dimension of original data to a low-dimensional subspace such as LPP and LFDA are widely used for clustering and classifications. These dimensionality reduction methods are formulated by minimization or maximization of a matrix trace and solved as few eigenvectors of the corresponding generalized eigenvalue problem. In this paper, based on the concept of the dimensionality reduction methods, we propose a novel supervised dimensionality reduction method that constructs a low-dimensional subspace with linear combinations of multiple eigenvectors. The proposed method needs to compute multiple eigenvectors; however, one can solve them efficiently by complex moment-based parallel eigensolvers. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | dimensionality reduction methods / supervised learning / linear combination of eigenvectors / complex moment-based eigensolvers |
| Paper # | IBISML2018-6 |
| Date of Issue | 2018-06-06 (IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
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| Conference Date | 2018/6/13(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Institute of Science and Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning Approach to Biodata Mining, and General |
| Chair | Yutaka Hirata(Chubu Univ.) / Hisashi Kashima(Kyoto Univ.) |
| Vice Chair | Hayaru Shouno(UEC) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Hayaru Shouno(Nagoya Univ.) / Masashi Sugiyama(NAIST) / Koji Tsuda(Nagoya Inst. of Tech.) / (AIST) |
| Assistant | Keiichiro Inagaki(Chubu Univ.) / Takashi Shinozaki(NICT) / Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Bioinformatics and Genomics / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A supervised dimensionality reduction method using linear combinations of multiple eigenvectors |
| Sub Title (in English) | |
| Keyword(1) | dimensionality reduction methods |
| Keyword(2) | supervised learning |
| Keyword(3) | linear combination of eigenvectors |
| Keyword(4) | complex moment-based eigensolvers |
| 1st Author's Name | Akira Imakura |
| 1st Author's Affiliation | University of Tsukuba(Univ. Tsukuba) |
| 2nd Author's Name | Momo Matsuda |
| 2nd Author's Affiliation | University of Tsukuba(Univ. Tsukuba) |
| 3rd Author's Name | Tetsuya Sakurai |
| 3rd Author's Affiliation | University of Tsukuba(Univ. Tsukuba) |
| Date | 2018-06-13 |
| Paper # | IBISML2018-6 |
| Volume (vol) | vol.118 |
| Number (no) | IBISML-81 |
| Page | pp.pp.39-45(IBISML), |
| #Pages | 7 |
| Date of Issue | 2018-06-06 (IBISML) |