Paper Abstract and Keywords |
Presentation |
2016-11-24 15:10
Modified Nearest Feature Space Approach for High Dimensional Data Sets Yang Lang Chang, Yung-Hao Lai, Tzu-Wei Tseng, Jyh-Perng Fang (NTUT) SANE2016-65 |
Abstract |
(in Japanese) |
(See Japanese page) |
(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) |
(in English) |
nearest feature space classifier / fisher criterion principle component analysis / / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 319, SANE2016-65, pp. 71-73, Nov. 2016. |
Paper # |
SANE2016-65 |
Date of Issue |
2016-11-17 (SANE) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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SANE2016-65 |
Conference Information |
Committee |
SANE |
Conference Date |
2016-11-24 - 2016-11-25 |
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 |
Paper Information |
Registration To |
SANE |
Conference Code |
2016-11-SANE |
Language |
English |
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 |
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nearest feature space classifier |
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fisher criterion principle component analysis |
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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 |
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National Taipei University of technology (NTUT) |
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Jyh-Perng Fang |
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National Taipei University of technology (NTUT) |
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Speaker |
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Date Time |
2016-11-24 15:10:00 |
Presentation Time |
20 minutes |
Registration for |
SANE |
Paper # |
SANE2016-65 |
Volume (vol) |
vol.116 |
Number (no) |
no.319 |
Page |
pp.71-73 |
#Pages |
3 |
Date of Issue |
2016-11-17 (SANE) |
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