2014 Fiscal Year Final Research Report
Feature extraction using Grassmann representation and its structural metric for brain signal processing
| Project/Area Number |
24700163
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Keywords | パターン識別 / 機械学習 / 生体信号処理 / 脳信号処理 |
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| Outline of Final Research Achievements |
We treat problems to extract intrinsic features efficiently from variational patterns, such that patterns are obtained as sequential images or faces, time series of brain signals. We approximate these patterns by linear subspaces, and investigate the structure of the set of linear spaces that is Grassmann manifold or Grassmannian. We introduced a probabilistic distance metric, Mahalanobis distance to Grassmannian to improve the classification performance. Furthermore, we extend the Grassmannian representation by using the soft-thresholding technique. The standard Grassmannian representation hardly divide the signal subspace and noise subspace, on the other hand, the extended Grassmannian representation softly divides them. We applied these proposed techniques to various classification problems including brain signal processing, and show its performance.
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| Free Research Field |
生体信号処理
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