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) |