On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis
Summary :
Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.5 pp.1204-1208
- Publication Date
- 2009/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1204
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence and Cognitive Science
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Keyword
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Masashi SUGIYAMA, "On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1204-1208, May 2009, doi: 10.1587/transinf.E92.D.1204.
Abstract: Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1204/_p
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@ARTICLE{e92-d_5_1204,
author={Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis},
year={2009},
volume={E92-D},
number={5},
pages={1204-1208},
abstract={Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.},
keywords={},
doi={10.1587/transinf.E92.D.1204},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 1204
EP - 1208
AU - Masashi SUGIYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.1204
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - Dimensionality reduction is one of the important preprocessing steps in practical pattern recognition. SEmi-supervised Local Fisher discriminant analysis (SELF)--which is a semi-supervised and local extension of Fisher discriminant analysis--was shown to work excellently in experiments. However, when data dimensionality is very high, a naive use of SELF is prohibitive due to high computational costs and large memory requirement. In this paper, we introduce computational tricks for making SELF applicable to large-scale problems.
ER -
English
