2016 Fiscal Year Final Research Report
Theoretical Study on Probabilistic Slow Feature Analysis and Its Applications to Recognition Functions
| Project/Area Number |
25730147
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Soft computing
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| Research Institution | Kobe University |
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Principal Investigator |
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Keywords | 統計的機械学習 / 高次元データ駆動科学 / 確率的時系列解析 / 情報統計力学 / ベイズ推論 / 深層学習 / ニューラルネットワーク / 情報計測 |
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| Outline of Final Research Achievements |
Due to recent developments in information technology and measurement technology, the data that we deal with have become large and high-dimensional. Therefore, it becomes more important to establish information processing techniques for extracting substantial information from high-dimensional time-series data. In this study, we have proposed statistical algorithms based on slow feature analysis in order to realize extraction of latent features and information recognitions in high-dimensional time-series data.
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| Free Research Field |
知能情報学，確率的情報処理，神経回路網理論，計算論的神経科学，データ駆動科学
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