| Presentation | 2023-06-29 Minorization-Maximization for Determinantal Point Processes Takahiro Kawashima, Hideitsu Hino, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A determinantal point process (DPP) is a powerful probabilistic model that generates diverse random subsets from a ground set. Since a DPP is characterized by a positive de?nite kernel, a DPP on a ?nite ground set can be parameterized by a kernel matrix. Recently, DPPs have gained attention in the machine learning community and have been applied to various practical problems; however, there is still room for further research on the learning of DPPs. In this paper, we propose a simple learning rule for full-rank DPPs based on a minorization-maximization (MM) algorithm, which monotonically increases the likelihood in each iteration. We show that our minorizer of the MM algorithm provides a tighter lower-bound compared to an existing method locally. In our experiments on both synthetic and real-world datasets, our method outperforms existing methods in most settings. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Determinantal point processes / Maximum likelihood estimation / MM algorithm / Recommender systems |
| Paper # | NC2023-7,IBISML2023-7 |
| Date of Issue | 2023-06-22 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
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| Conference Date | 2023/6/29(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | OIST Conference Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hirokazu Tanaka(Tokyo City Univ.) / Masashi Sugiyama(Univ. of Tokyo) |
| Vice Chair | Jun Izawa(Univ. of Tsukub) / Toshihiro Kamishima(AIST) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Jun Izawa(NTT) / Toshihiro Kamishima(NAIST) / Koji Tsuda(NTT) / (Hokkaido Univ.) |
| Assistant | Yoshimasa Tawatsuji(Waseda Univ.) / Takato Horii(Osaka Univ.) / Yoshinobu Kawahara(Osaka Univ.) / Taiji Suzuki(Tokyo Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Information-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) | Minorization-Maximization for Determinantal Point Processes |
| Sub Title (in English) | |
| Keyword(1) | Determinantal point processes |
| Keyword(2) | Maximum likelihood estimation |
| Keyword(3) | MM algorithm |
| Keyword(4) | Recommender systems |
| 1st Author's Name | Takahiro Kawashima |
| 1st Author's Affiliation | The Graduate University for Advanced Studies, SOKENDAI(SOKENDAI) |
| 2nd Author's Name | Hideitsu Hino |
| 2nd Author's Affiliation | The Institute of Statistical Mathematics/RIKEN(ISM/RIKEN) |
| Date | 2023-06-29 |
| Paper # | NC2023-7,IBISML2023-7 |
| Volume (vol) | vol.123 |
| Number (no) | NC-90,IBISML-91 |
| Page | pp.pp.39-47(NC), pp.39-47(IBISML), |
| #Pages | 9 |
| Date of Issue | 2023-06-22 (NC, IBISML) |