A novel LDA approach for high-dimensional data
G Feng, D Hu, M Li, Z Zhou - … , ICNC 2005, Changsha, China, August 27 …, 2005 - Springer
Abstract Linear Discriminant Analysis (LDA) is one of the most popular linear projection
techniques for feature extraction. The major drawback of this method is that it may encounter …
techniques for feature extraction. The major drawback of this method is that it may encounter …
[PDF][PDF] Face recognition using pca and lda with singular value decomposition (svd) using 2dlda
N Nain, P Gour, N Agarwal, RP Talawar… - Proceedings of the World …, 2008 - Citeseer
Linear Discriminant Analysis (LDA) is well-known scheme for feature extraction and
dimension reduction. It has been used widely in many applications involving high …
dimension reduction. It has been used widely in many applications involving high …
An efficient algorithm to solve the small sample size problem for LDA
W Zheng, L Zhao, C Zou - Pattern Recognition, 2004 - Elsevier
An efficient algorithm to solve the small sample size problem for LDA - ScienceDirect Skip to
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Improved discriminate analysis for high-dimensional data and its application to face recognition
Many pattern recognition applications involve the treatment of high-dimensional data and
the small sample size problem. Principal component analysis (PCA) is a common used …
the small sample size problem. Principal component analysis (PCA) is a common used …
An adaptive nonparametric discriminant analysis method and its application to face recognition
Abstract Linear Discriminant Analysis (LDA) is frequently used for dimension reduction and
has been successfully utilized in many applications, especially face recognition. In classical …
has been successfully utilized in many applications, especially face recognition. In classical …
An improved LDA algorithm and its application to face recognition
Z LIU - Computer Engineering & Science, 2011 - joces.nudt.edu.cn
Linear discriminant analysis (LDA) is a typical feature extraction method, but there exist at
least two critical drawbacks in LDA: the small sample size problem and the rank limitation …
least two critical drawbacks in LDA: the small sample size problem and the rank limitation …
Two-dimensional linear discriminant analysis
Abstract Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction
and dimension reduction. It has been used widely in many applications involving high …
and dimension reduction. It has been used widely in many applications involving high …
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …
DLDA/QR: a robust direct LDA algorithm for face recognition and its theoretical foundation
Feature extraction is one of the hot topics in face recognition. However, many face extraction
methods will suffer from the “small sample size” problem, such as Linear Discriminant …
methods will suffer from the “small sample size” problem, such as Linear Discriminant …
Discriminant feature extraction based on center distance
In this paper, a novel discriminant feature extraction algorithm employing center-based
distance is proposed for face recognition. This new method, which is a supervised linear …
distance is proposed for face recognition. This new method, which is a supervised linear …