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| Presentation | 2019-06-17 16:15 Imputation of Missing Time-Series Multimodal Data with Variational Autoencoder Ryoichi Kojima, Shinya Wada, Kiyohito Yoshihara (KDDI Research)  IBISML2019-8 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (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) |
| (in English) | Variational Autoencoder / Time-Series Multimodal Data / Imputation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 89, IBISML2019-8, pp. 51-55, June 2019. | |
| Paper # | IBISML2019-8 | |
| Date of Issue | 2019-06-10 (IBISML) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | IBISML2019-8 |
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| Conference Information | |
| Committee | NC IBISML IPSJ-MPS IPSJ-BIO |
| Conference Date | 2019-06-17 - 2019-06-19 |
| 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 |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2019-06-NC-IBISML-MPS-BIO |
| Language | Japanese |
| 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 |
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| 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) |
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| Speaker | Author-1 |
| Date Time | 2019-06-17 16:15:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2019-8 |
| Volume (vol) | vol.119 |
| Number (no) | no.89 |
| Page | pp.51-55 |
| #Pages | 5 |
| Date of Issue | 2019-06-10 (IBISML) |
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