| 講演名 | 2016-11-24 Modified Nearest Feature Space Approach for High Dimensional Data Sets Yang Lang Chang(NTUT), Yung-Hao Lai(NTUT), Tzu-Wei Tseng(NTUT), Jyh-Perng Fang(NTUT), |
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| 抄録(和) | 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. |
| 抄録(英) | 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. |
| キーワード(和) | nearest feature space classifier / fisher criterion principle component analysis |
| キーワード(英) | nearest feature space classifier / fisher criterion principle component analysis |
| 資料番号 | SANE2016-65 |
| 発行日 | 2016-11-17 (SANE) |
| 研究会情報 | |
| 研究会 | SANE |
|---|---|
| 開催期間 | 2016/11/24(から2日開催) |
| 開催地（和） | 国立台北科技大学 |
| 開催地（英） | National Taipei University of Technology (NTUT) |
| テーマ（和） | ICSANE2016 |
| テーマ（英） | ICSANE2016 |
| 委員長氏名（和） | 小林 弘一(阪工大) |
| 委員長氏名（英） | Hirokazu Kobayashi(Osaka Inst. of Tech.) |
| 副委員長氏名（和） | 水野 貴秀(JAXA) / 森山 敏文(長崎大) |
| 副委員長氏名（英） | Takahide Mizuno(JAXA) / Toshifumi Moriyama(Nagasaki Univ.) |
| 幹事氏名（和） | 牧 謙一郎(JAXA) / 小幡 康(三菱電機) |
| 幹事氏名（英） | Kenichiro Maki(JAXA) / Yasushi Obata(Mitsubishi Electric) |
| 幹事補佐氏名（和） | 毛塚 敦(電子航法研) / 秋田 学(電通大) |
| 幹事補佐氏名（英） | Atsushi Kezuka(ENRI) / Manabu Akita(Univ. of Electro-Comm.) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Space, Aeronautical and Navigational Electronics |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Modified Nearest Feature Space Approach for High Dimensional Data Sets |
| サブタイトル（和） | |
| キーワード(1)（和/英） | nearest feature space classifier / nearest feature space classifier |
| キーワード(2)（和/英） | fisher criterion principle component analysis / fisher criterion principle component analysis |
| 第 1 著者 氏名（和/英） | Yang Lang Chang / Yang Lang Chang |
| 第 1 著者 所属（和/英） | National Taipei University of technology(略称：NTUT) National Taipei University of technology(略称：NTUT) |
| 第 2 著者 氏名（和/英） | Yung-Hao Lai / Yung-Hao Lai |
| 第 2 著者 所属（和/英） | National Taipei University of technology(略称：NTUT) National Taipei University of technology(略称：NTUT) |
| 第 3 著者 氏名（和/英） | Tzu-Wei Tseng / Tzu-Wei Tseng |
| 第 3 著者 所属（和/英） | National Taipei University of technology(略称：NTUT) National Taipei University of technology(略称：NTUT) |
| 第 4 著者 氏名（和/英） | Jyh-Perng Fang / Jyh-Perng Fang |
| 第 4 著者 所属（和/英） | National Taipei University of technology(略称：NTUT) National Taipei University of technology(略称：NTUT) |
| 発表年月日 | 2016-11-24 |
| 資料番号 | SANE2016-65 |
| 巻番号（vol） | vol.116 |
| 号番号（no） | SANE-319 |
| ページ範囲 | pp.71-73(SANE), |
| ページ数 | 3 |
| 発行日 | 2016-11-17 (SANE) |