| Presentation | 2015-06-23 Efficient sparse learning for combinatorial model by using safe screening approach Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Ichiro Takeuchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In a variety of machine learning tasks, it has been desired to incorporate high-order interaction effects of multiple covariates. However, for recent applications with a large number covariates, it is highly challenging to identify important high-order interaction features since the number of possible candidates would be extremely large. In this paper, we propose an efficient algorithm for LASSO-based sparse learning of such high-order interaction models. The basic strategy is to use a recently introduced safe feature screening technique by which a subset of non-active features can be identified and they can be screened-out prior to LASSO training. However, applying safe feature screening to each of the extremely large number of high-order interaction features would be computationally infeasible. Our key idea for solving this computational issue is to exploit the underlying tree structure among high-order interaction features. Specifically, we introduce a set of pruning conditions of the tree such that, if one of the conditions is satis ed in a certain node, then all the high-order interaction features corresponding to its descendant nodes can be guaranteed to be non-active at the optimal solution, and they can be screened-out prior to LASSO training. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | high-order interaction model / LASSO / sparse learning / safe-screening |
| Paper # | IBISML2015-10 |
| Date of Issue | 2015-06-16 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2015/6/23(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 | Toshimichi Saito(Hosei Univ.) / Masakazu Sekijima(東工大) / Takashi Washio(Osaka Univ.) / Hayaru Shouno(電通大) |
| Vice Chair | Shigeo Sato(Tohoku Univ.) / / Kenji Fukumizu(ISM) / Masashi Sugiyama(Tokyo Inst. of Tech.) |
| Secretary | Shigeo Sato(Kyushu Inst. of Tech.) / (Kyoto Sangyo Univ.) / Kenji Fukumizu(京大) / Masashi Sugiyama(お茶の水女子大) / (OIST) |
| Assistant | Hiroyuki Kanbara(Tokyo Inst. of Tech.) / Hisanao Akima(Tohoku Univ.) / / Koji Tsuda(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| 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) | Efficient sparse learning for combinatorial model by using safe screening approach |
| Sub Title (in English) | |
| Keyword(1) | high-order interaction model |
| Keyword(2) | LASSO |
| Keyword(3) | sparse learning |
| Keyword(4) | safe-screening |
| 1st Author's Name | Kazuya Nakagawa |
| 1st Author's Affiliation | Nagoya Institute of Technology(NIT) |
| 2nd Author's Name | Shinya Suzumura |
| 2nd Author's Affiliation | Nagoya Institute of Technology(NIT) |
| 3rd Author's Name | Masayuki Karasuyama |
| 3rd Author's Affiliation | Nagoya Institute of Technology(NIT) |
| 4th Author's Name | Ichiro Takeuchi |
| 4th Author's Affiliation | Nagoya Institute of Technology(NIT) |
| Date | 2015-06-23 |
| Paper # | IBISML2015-10 |
| Volume (vol) | vol.115 |
| Number (no) | IBISML-112 |
| Page | pp.pp.63-68(IBISML), |
| #Pages | 6 |
| Date of Issue | 2015-06-16 (IBISML) |