Presentation | 1994/1/28 Feature Extraction from High-Dimensional Data-Evaluation of the Effect of smoothing- Senya Kiyasu, Sadao Fujimura, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In order to efficiently and accurately obtain the results from high-dimensional data,significant features should be extracted before processing.We already proposed a feature extraction method for significance weighted supervised classification.But the weighting factors for each dimensions,which are the results of the feature extraction,were not smooth enough along the dimension.In order to reduce the influence of random noise,we smoothed the data by moving average method and used for feature extraction.The extracted weighting factors were much smoother by the effect of noise reduction. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | feature extraction / high-dimensional data / smoothing / noise / classification |
Paper # | SANE93-79 |
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Committee | SANE |
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Conference Date | 1994/1/28(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Space, Aeronautical and Navigational Electronics (SANE) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Feature Extraction from High-Dimensional Data-Evaluation of the Effect of smoothing- |
Sub Title (in English) | |
Keyword(1) | feature extraction |
Keyword(2) | high-dimensional data |
Keyword(3) | smoothing |
Keyword(4) | noise |
Keyword(5) | classification |
1st Author's Name | Senya Kiyasu |
1st Author's Affiliation | Department of Mathematical Engineering and Information Physics, Faculty of Engineering,University of Tokyo() |
2nd Author's Name | Sadao Fujimura |
2nd Author's Affiliation | Department of Mathematical Engineering and Information Physics, Faculty of Engineering,University of Tokyo |
Date | 1994/1/28 |
Paper # | SANE93-79 |
Volume (vol) | vol.93 |
Number (no) | 453 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |