Improved linear discriminant analysis method
ZB Liu, ST YU - Journal of Computer Applications, 2011 - joca.cn
Linear Discriminant Analysis (LDA) is an effective feature extraction method, but there exist
at least two critical drawbacks in it: small sample size problem and rank limitation. In order to …
at least two critical drawbacks in it: small sample size problem and rank limitation. In order to …
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 …
An improved linear discriminant analysis method and its application to face recognition
K Li, P Tang - Applied Mechanics and Materials, 2014 - Trans Tech Publ
Linear discriminant analysis (LDA) is an important feature extraction method. This paper
proposes an improved linear discriminant analysis method, which redefines the within-class …
proposes an improved linear discriminant analysis method, which redefines the within-class …
Facial feature extraction method based on coefficients of variances
FX Song, D Zhang, CK Chen, JY Yang - Journal of Computer Science and …, 2007 - Springer
Abstract Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are
two popular feature extraction techniques in statistical pattern recognition field. Due to small …
two popular feature extraction techniques in statistical pattern recognition field. Due to small …
Linear Laplacian discrimination for feature extraction
Discriminant feature extraction plays a fundamental role in pattern recognition. In this paper,
we propose the linear Laplacian discrimination (LLD) algorithm/or discriminant feature …
we propose the linear Laplacian discrimination (LLD) algorithm/or discriminant feature …
A parameterized direct LDA and its application to face recognition
F Song, D Zhang, J Wang, H Liu, Q Tao - Neurocomputing, 2007 - Elsevier
In this paper, we propose a new feature extraction method—parameterized direct linear
discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D …
discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D …
Local Similarity based Discriminant Analysis for Face Recognition
Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for
feature extraction and has been widely applied in face recognition. However, it cannot be …
feature extraction and has been widely applied in face recognition. However, it cannot be …
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 …
Feature extraction using a fast null space based linear discriminant analysis algorithm
GF Lu, Y Wang - Information Sciences, 2012 - Elsevier
The small sample size problem is often encountered in pattern recognition. Several
algorithms for null space based linear discriminant analysis (NLDA) have been developed to …
algorithms for null space based linear discriminant analysis (NLDA) have been developed to …