| Presentation | 2019-06-17 Imputation of Missing Time-Series Multimodal Data with Variational Autoencoder Ryoichi Kojima, Shinya Wada, Kiyohito Yoshihara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Data is often missing and that results in negative effects on subsequent data analysis and creating machine learning model. In this paper, we propose the missing-data imputation method that is able to deal with time-series multimodal data called ``tsMVAE (time-series multimodal variational autoencoder)''.The tsMVAE inferences the identical latent representation with VAE(Variational Autoencoder),and generates time-series multimodal data including missing ones while avoiding any bias.Furthermore, by regarding human activities as missing modal data,the tsMVAE also estimates human activities.We do two experiments with the time-series multimodal sensor dataset : ``OPPORTUNITY Activity Recognition Data Set''.The tsMVAE outperforms the conventional algorithms in terms of imputation more precisely and performs equivalent to the conventional models in terms of estimating human activities. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Variational Autoencoder / Time-Series Multimodal Data / Imputation |
| Paper # | IBISML2019-8 |
| Date of Issue | 2019-06-10 (IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-MPS / IPSJ-BIO |
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| Conference Date | 2019/6/17(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) | Neurocomputing, Machine Learning Approach to Biodata Mining, and General |
| Chair | Hayaru Shouno(UEC) / Hisashi Kashima(Kyoto Univ.) / Masakazu Sekijima(Tokyo Tech) / Hiroyuki Kurata(Kyutech) |
| Vice Chair | Kazuyuki Samejima(Tamagawa Univ) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Kazuyuki Samejima(NAIST) / Masashi Sugiyama(NTT) / Koji Tsuda(Nagoya Inst. of Tech.) / (AIST) / (Nagoya Univ.) |
| Assistant | Takashi Shinozaki(NICT) / Ken Takiyama(TUAT) / Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / IPSJ Special Interest Group on Mathematical Modeling and Problem Solving / IPSJ Special Interest Group on Bioinformatics and Genomics |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Imputation of Missing Time-Series Multimodal Data with Variational Autoencoder |
| Sub Title (in English) | |
| Keyword(1) | Variational Autoencoder |
| Keyword(2) | Time-Series Multimodal Data |
| Keyword(3) | Imputation |
| 1st Author's Name | Ryoichi Kojima |
| 1st Author's Affiliation | KDDI Research, Inc.(KDDI Research) |
| 2nd Author's Name | Shinya Wada |
| 2nd Author's Affiliation | KDDI Research, Inc.(KDDI Research) |
| 3rd Author's Name | Kiyohito Yoshihara |
| 3rd Author's Affiliation | KDDI Research, Inc.(KDDI Research) |
| Date | 2019-06-17 |
| Paper # | IBISML2019-8 |
| Volume (vol) | vol.119 |
| Number (no) | IBISML-89 |
| Page | pp.pp.51-55(IBISML), |
| #Pages | 5 |
| Date of Issue | 2019-06-10 (IBISML) |