IEICE TRANSACTIONS on Information
Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation
Summary :
We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.5 pp.1396-1399
- Publication Date
- 2016/05/01
- Publicized
- 2016/02/12
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8243
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Hyunki LIM
Chung-Ang University
Jaesung LEE
Chung-Ang University
Dae-Won KIM
Chung-Ang University
Keyword
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Hyunki LIM, Jaesung LEE, Dae-Won KIM, "Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 5, pp. 1396-1399, May 2016, doi: 10.1587/transinf.2015EDL8243.
Abstract: We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8243/_p
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@ARTICLE{e99-d_5_1396,
author={Hyunki LIM, Jaesung LEE, Dae-Won KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation},
year={2016},
volume={E99-D},
number={5},
pages={1396-1399},
abstract={We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.},
keywords={},
doi={10.1587/transinf.2015EDL8243},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation
T2 - IEICE TRANSACTIONS on Information
SP - 1396
EP - 1399
AU - Hyunki LIM
AU - Jaesung LEE
AU - Dae-Won KIM
PY - 2016
DO - 10.1587/transinf.2015EDL8243
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2016
AB - We propose a multi-label feature selection method that considers feature dependencies. The proposed method circumvents the prohibitive computations by using a low-rank approximation method. The empirical results acquired by applying the proposed method to several multi-label datasets demonstrate that its performance is comparable to those of recent multi-label feature selection methods and that it reduces the computation time.
ER -
English
