| Presentation | 2016-09-05 Sparse learning for pattern mining problem by using Safe Pattern Pruning method Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Koji Tsuda, Ichiro Takeuchi, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model. The advantage of the SPP method over existing boosting-type method is that the former can find the superset by a single search over the database, while the latter requires multiple searches. The SPP method is inspired by recent development of safe feature screening. In order to extend the idea of safe feature screening into predictive pattern mining, we derive a novel pruning rule called safe pattern pruning (SPP) rule that can be used for searching over the tree defined among patterns in the database. The SPP rule has a property that,if a node corresponding to a pattern in the database is pruned out by the SPP rule,then it is guaranteed that all the patterns corresponding to its descendant nodes are never needed for the optimal predictive model. We apply the SPP method to graph mining and item-set mining problems, and demonstrate its computational advantage. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Pattern mining / Sparse learning / Safe screening / Convex optimization |
| Paper # | PRMU2016-70,IBISML2016-25 |
| Date of Issue | 2016-08-29 (PRMU, IBISML) |
| Conference Information | |
| Committee | PRMU / IPSJ-CVIM / IBISML |
|---|---|
| Conference Date | 2016/9/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Eisaku Maeda(NTT) / / Kenji Fukumizu(ISM) |
| Vice Chair | Seiichi Uchida(Kyushu Univ.) / Hironobu Fujiyoshi(Chubu Univ.) / / Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Seiichi Uchida(Kyoto Univ.) / Hironobu Fujiyoshi(NTT) / / Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Nagoya Inst. of Tech.) |
| Assistant | Masaki Oonishi(AIST) / Takuya Funatomi(NAIST) / / Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media / Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Sparse learning for pattern mining problem by using Safe Pattern Pruning method |
| Sub Title (in English) | |
| Keyword(1) | Pattern mining |
| Keyword(2) | Sparse learning |
| Keyword(3) | Safe screening |
| Keyword(4) | Convex optimization |
| 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 | Koji Tsuda |
| 4th Author's Affiliation | University of Tokyo(Univ. of Tokyo) |
| 5th Author's Name | Ichiro Takeuchi |
| 5th Author's Affiliation | Nagoya Institute of Technology(NIT) |
| Date | 2016-09-05 |
| Paper # | PRMU2016-70,IBISML2016-25 |
| Volume (vol) | vol.116 |
| Number (no) | PRMU-208,IBISML-209 |
| Page | pp.pp.127-134(PRMU), pp.127-134(IBISML), |
| #Pages | 8 |
| Date of Issue | 2016-08-29 (PRMU, IBISML) |