| Presentation | 2021-06-28 Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic approach to the analysis of aggregated data. One of the most important operations in CGM is maximum a posteriori (MAP) inference of unobserved variables under given observations. Because the MAP inference problem for general CGMs has been shown to be NP-hard, an approach that solves an approximate problem has been proposed. However, this approach has two major drawbacks. First, the quality of the solution deteriorates when the values in the count tables are small, because the approximation becomes inaccurate. Second, since continuous relaxation is applied, the integrality constraints of the output are violated. To resolve these problems, this paper proposes a new method for MAP inference for CGMs on path graphs. Our method is based on the Difference of Convex Algorithm (DCA), which is a general methodology to minimize a function represented as the sum of a convex function and a concave function. In our algorithm, important subroutines in DCA can be efficiently calculated by minimum convex cost flow algorithms. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Aggregated Data / Collective Graphical Model / DC Algorithm / Minimum Convex Cost Flow |
| Paper # | NC2021-10,IBISML2021-10 |
| Date of Issue | 2021-06-21 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
|---|---|
| Conference Date | 2021/6/28(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Rieko Osu(Waseda Univ.) / Ichiro Takeuchi(Nagoya Inst. of Tech.) / 倉田 博之(九工大) / 関嶋 政和(東工大) |
| Vice Chair | Hiroshi Yamakawa(Univ of Tokyo) / Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Hiroshi Yamakawa(ATR) / Masashi Sugiyama(NICT) / (Univ. of Tokyo) / (AIST) |
| Assistant | Nobuhiko Wagatsuma(Toho Univ.) / Tomoki Kurikawa(KMU) / Tomoharu Iwata(NTT) / Atsuyoshi Nakamura(Hokkaido 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 |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm |
| Sub Title (in English) | |
| Keyword(1) | Aggregated Data |
| Keyword(2) | Collective Graphical Model |
| Keyword(3) | DC Algorithm |
| Keyword(4) | Minimum Convex Cost Flow |
| 1st Author's Name | Yasunori Akagi |
| 1st Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 2nd Author's Name | Naoki Marumo |
| 2nd Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 3rd Author's Name | Hideaki Kim |
| 3rd Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 4th Author's Name | Takeshi Kurashima |
| 4th Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 5th Author's Name | Hiroyuki Toda |
| 5th Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| Date | 2021-06-28 |
| Paper # | NC2021-10,IBISML2021-10 |
| Volume (vol) | vol.121 |
| Number (no) | NC-79,IBISML-80 |
| Page | pp.pp.70-77(NC), pp.70-77(IBISML), |
| #Pages | 8 |
| Date of Issue | 2021-06-21 (NC, IBISML) |