| Presentation | 2016-11-24 Modified Nearest Feature Space Approach for High Dimensional Data Sets Yang Lang Chang, Yung-Hao Lai, Tzu-Wei Tseng, Jyh-Perng Fang, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | With the progress of remote sensing technology, the high volume of high dimensional data sets is increased rapidly. Thus, it is important to better analyze and particular process these huge datasets. Several studies have been investigated to the classification algorithm of high dimensional data sets, such as nearest feature space (NFS). NFS can provide more information to virtually enlarge the training samples set. However, NFS is difficult to categorize and easily cause misjudgments when test point is too close with the distribution of sample points. In this paper, we proposed a new method called modified nearest feature space (MNFS), which can analysis the coverage of the feature space (FS) used in NFS and limit the extensible range of each FS in line, to reduce the impact of the overlapping between each category. Experimental results prove that MNFS is better than NFS on classification accuracy. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | nearest feature space classifier / fisher criterion principle component analysis |
| Paper # | SANE2016-65 |
| Date of Issue | 2016-11-17 (SANE) |
| Conference Information | |
| Committee | SANE |
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| Conference Date | 2016/11/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | National Taipei University of Technology (NTUT) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | ICSANE2016 |
| Chair | Hirokazu Kobayashi(Osaka Inst. of Tech.) |
| Vice Chair | Takahide Mizuno(JAXA) / Toshifumi Moriyama(Nagasaki Univ.) |
| Secretary | Takahide Mizuno(JAXA) / Toshifumi Moriyama(Mitsubishi Electric) |
| Assistant | Atsushi Kezuka(ENRI) / Manabu Akita(Univ. of Electro-Comm.) |
| Paper Information | |
| Registration To | Technical Committee on Space, Aeronautical and Navigational Electronics |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Modified Nearest Feature Space Approach for High Dimensional Data Sets |
| Sub Title (in English) | |
| Keyword(1) | nearest feature space classifier |
| Keyword(2) | fisher criterion principle component analysis |
| 1st Author's Name | Yang Lang Chang |
| 1st Author's Affiliation | National Taipei University of technology(NTUT) |
| 2nd Author's Name | Yung-Hao Lai |
| 2nd Author's Affiliation | National Taipei University of technology(NTUT) |
| 3rd Author's Name | Tzu-Wei Tseng |
| 3rd Author's Affiliation | National Taipei University of technology(NTUT) |
| 4th Author's Name | Jyh-Perng Fang |
| 4th Author's Affiliation | National Taipei University of technology(NTUT) |
| Date | 2016-11-24 |
| Paper # | SANE2016-65 |
| Volume (vol) | vol.116 |
| Number (no) | SANE-319 |
| Page | pp.pp.71-73(SANE), |
| #Pages | 3 |
| Date of Issue | 2016-11-17 (SANE) |