Approximate LDA Technique for Dimensionality Reduction in the Small Sample Size Case

被引：11

作者：

Paliwal, Kuldip K. ^{[1
]}

Sharma, Alok ^{[2
,3
,4
]}

机构：

[1] Griffith Univ, Sch Engn, Brisbane, Qld, Australia

[2] Univ Tokyo, Tokyo, Japan

[3] Griffith Univ, Signal Proc Lab, Brisbane, Qld, Australia

[4] Univ South Pacific, Suva, Fiji

来源：

JOURNAL OF PATTERN RECOGNITION RESEARCH | 2011年 / 6卷 / 02期

关键词：

Approximate linear discriminant analysis (ALDA); dimensionality reduction; small sample size problem; classification accuracy; regularized LDA;

D O I：

10.13176/11.370

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The regularized linear discriminant analysis (LDA) technique overcomes the small sample size (SSS) problem by adding a regularization parameter to the eigenvalues of within-class scatter matrix. However, it has some drawbacks. In this paper we address its drawbacks and propose an improvement. The proposed technique is experimented on several datasets and promising results have been obtained.

引用

页码：298 / 306

页数：9

共 50 条

[31] On a solution for the high-dimensionality-small-sample-size regression problem with several different microarrays
Nikulin, Vladimir
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2014, 9 (03) : 221 - 234
[32] Intrinsic dimensionality and small sample properties of classifiers
Raudys, S
KYBERNETIKA, 1998, 34 (04) : 461 - 466
[33] SVAD: Stacked Variational Autoencoder Deep Neural Network-Based Dimensionality Reduction and Classification of Small Sample Size and High Dimensional Data
Neha Srivastava
Devendra K. Tayal
SN Computer Science, 5 (7)
[34] A Small Maritime Target Detection Method Using Nonlinear Dimensionality Reduction and Feature Sample Distance
Guan, Jian
Jiang, Xingyu
Liu, Ningbo
Ding, Hao
Dong, Yunlong
Guo, Zhongping
REMOTE SENSING, 2024, 16 (16)
[35] Dimensionality reduction through LDA and bag-of-features applied to image retrieval
Gonzalez Valenzuela, Ricardo E.
Pedrini, Helio
Schwartz, William Robson
COMPUTATIONAL VISION AND MEDICAL IMAGE PROCESSING IV, 2014, : 31 - 37
[36] Network-based dimensionality reduction of high-dimensional, low-sample-size datasets
Kosztyan, Zsolt T.
Kurbucz, Marcell T.
Katona, Attila I.
KNOWLEDGE-BASED SYSTEMS, 2022, 251
[37] A new LDA-based face recognition system which can solve the small sample size problem
Chen, LF
Liao, HYM
Ko, MT
Lin, JC
Yu, GJ
PATTERN RECOGNITION, 2000, 33 (10) : 1713 - 1726
[38] WAVELET-FACE BASED SUBSPACE LDA METHOD TO SOLVE SMALL SAMPLE SIZE PROBLEM IN FACE RECOGNITION
Chen, Wen-Sheng
Huang, Jian
Zou, Jin
Fang, Bin
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2009, 7 (02) : 199 - 214
[39] A TECHNIQUE FOR THE REDUCTION OF DIMENSIONALITY IN DYNAMIC-PROGRAMMING
BELLMAN, R
ROOSTA, M
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 1982, 88 (02) : 543 - 546
[40] APPROXIMATE DETERMINATION OF OPTIMAL RANDOM SAMPLE SIZE
BANDEMER, H
METRIKA, 1970, 15 (03) : 97 - 104

← 1 2 3 4 5 →