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| Presentation | 2021-07-09 13:25 A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables -- Bayes Optimal Prediction and Sub-Optimal Algorithm -- Sho Kayama (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.)  IT2021-24 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | By considering the probability model with the structure that the data is divided into clusters and each cluster has an independent linear regression model, it is shown that various previous studies of extensions of the linear regression model can be organized from a unified perspective. Furthermore, under the assumption of this data generating probability model, an approximation algorithm using optimal prediction and variational Bayes under the Bayesian criterion is derived. However, this algorithm holds the problem that the computational complexity of updating parameters increases when the number of observed data is large. In this paper, we propose the reduction method of computational complexity on the idea on stochastic gradient descent. In addition, we conduct an experiment for confirming the behavior of the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Variational Inference / Bayes Optimal Prediction / Stochastic Gradient Descent / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 96, IT2021-24, pp. 51-56, July 2021. | |
| Paper # | IT2021-24 | |
| Date of Issue | 2021-07-01 (IT) | |
| 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 | IT2021-24 |
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| Conference Information | |
| Committee | IT |
| Conference Date | 2021-07-08 - 2021-07-09 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Freshman session, General |
| Paper Information | |
| Registration To | IT |
| Conference Code | 2021-07-IT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables |
| Sub Title (in English) | Bayes Optimal Prediction and Sub-Optimal Algorithm |
| Keyword(1) | Variational Inference |
| Keyword(2) | Bayes Optimal Prediction |
| Keyword(3) | Stochastic Gradient Descent |
| Keyword(4) | |
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| 1st Author's Name | Sho Kayama |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Shota Saito |
| 2nd Author's Affiliation | Gunma University (Gunma Univ.) |
| 3rd Author's Name | Toshiyasu Matsushima |
| 3rd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-07-09 13:25:00 |
| Presentation Time | 25 minutes |
| Registration for | IT |
| Paper # | IT2021-24 |
| Volume (vol) | vol.121 |
| Number (no) | no.96 |
| Page | pp.51-56 |
| #Pages | 6 |
| Date of Issue | 2021-07-01 (IT) |
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