| Presentation | 2016-07-05 Non-linear Embedded Feature Extraction Method using Comb-shaped Neural Network Akihito Sudo, Tomoyuki Higuchi, Shin'ya Nakano, Masaya Saito, Takahiro Yabe, Yoshihide Sekimoto, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Feature selection methods can be divided into three categories; wrapper methods, filter methods, and embedded methods. Embedded methods recently attract many researchers due to the effectiveness for the sparse modeling. One of important issues in researches of the embedded methods are devising non-linear methods, and methods employing non-linear kernel has been proposed. In the present paper, we propose a non-linear embedded feature selection method employing deep neural network. In the experiment using two synthetic datasets, the proposed method outperforms Lasso in terms of both feature selection and function approximation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Deep Learning / Sparse Modeling / Feature Selection / Lasso / Nonlinear |
| Paper # | IBISML2016-1 |
| Date of Issue | 2016-06-28 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2016/7/4(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 | Shigeo Sato(Tohoku Univ.) / / Kenji Fukumizu(ISM) |
| Vice Chair | Masafumi Hagiwara(Keio Univ.) / / Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Masafumi Hagiwara(Kyoto Sangyo Univ.) / (Tokyo Inst. of Tech.) / Masashi Sugiyama / Hisashi Kashima(Univ. of Tokyo) / (Nagoya Inst. of Tech.) |
| Assistant | Hisanao Akima(Tohoku Univ.) / Yoshihisa Shinozawa(Keio Univ.) / / Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Special Interest Group on Bioinformatics and Genomics / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / 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) | Non-linear Embedded Feature Extraction Method using Comb-shaped Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Deep Learning |
| Keyword(2) | Sparse Modeling |
| Keyword(3) | Feature Selection |
| Keyword(4) | Lasso |
| Keyword(5) | Nonlinear |
| 1st Author's Name | Akihito Sudo |
| 1st Author's Affiliation | the University of Tokyo(UT) |
| 2nd Author's Name | Tomoyuki Higuchi |
| 2nd Author's Affiliation | the Institute of Statistical Mathematics(ISM) |
| 3rd Author's Name | Shin'ya Nakano |
| 3rd Author's Affiliation | the Institute of Statistical Mathematics(ISM) |
| 4th Author's Name | Masaya Saito |
| 4th Author's Affiliation | the Institute of Statistical Mathematics(ISM) |
| 5th Author's Name | Takahiro Yabe |
| 5th Author's Affiliation | the University of Tokyo(UT) |
| 6th Author's Name | Yoshihide Sekimoto |
| 6th Author's Affiliation | the University of Tokyo(UT) |
| Date | 2016-07-05 |
| Paper # | IBISML2016-1 |
| Volume (vol) | vol.116 |
| Number (no) | IBISML-121 |
| Page | pp.pp.127-131(IBISML), |
| #Pages | 5 |
| Date of Issue | 2016-06-28 (IBISML) |