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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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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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 / 確率的Slow Feature Analysis / データ駆動科学 / 知的学習論 / 状態空間モデル / センサデータ / 潜在情報抽出 / 数理データサイエンス / 情報認識 / 確率的情報処理 / 動画像認識 / 機械学習 / 多次元データ / データ駆動型アプローチ / ダイナミクス / 潜在ダイナミクス / 人工知能 / ベイズ統計学 / 教師なし学習 / 空間認識 / 場所細胞 / 確率伝搬法 / EMアルゴリズム / 情報抽出 / 時系列解析 / ビッグデータ |
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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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Report
(5 results)
Research Products
(72 results)
