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 …
A new and fast implementation for null space based linear discriminant analysis
In this paper we present a new implementation for the null space based linear discriminant
analysis. The main features of our implementation include:(i) the optimal transformation …
analysis. The main features of our implementation include:(i) the optimal transformation …
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
main contentSkip to article Elsevier logo Journals & Books Search RegisterSign in View …
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A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices
A Sharma, KK Paliwal - Pattern Recognition, 2012 - Elsevier
Null linear discriminant analysis (LDA) method is a popular dimensionality reduction method
for solving small sample size problem. The implementation of null LDA method is, however …
for solving small sample size problem. The implementation of null LDA method is, however …
Whitened LDA for face recognition
VDM Nhat, SY Lee, HY Youn - Proceedings of the 6th ACM international …, 2007 - dl.acm.org
Over the years, many Linear Discriminant Analysis (LDA) algorithms have been proposed
for the study of high dimensional data in a large variety of problems. An intrinsic limitation of …
for the study of high dimensional data in a large variety of problems. An intrinsic limitation of …
Efficient kernel discriminant analysis via QR decomposition
Abstract Linear Discriminant Analysis (LDA) is a well-known method for fea-ture extraction
and dimension reduction. It has been used widely in many applications such as face …
and dimension reduction. It has been used widely in many applications such as face …
Incremental learning of discriminant common vectors for feature extraction
GF Lu, J Zou, Y Wang - Applied Mathematics and Computation, 2012 - Elsevier
Discriminant common vectors (DCV), which can effectively extract the features of face
images, is a recently proposed algorithm to overcome the small sample size (SSS) problem …
images, is a recently proposed algorithm to overcome the small sample size (SSS) problem …
[PDF][PDF] Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis.
Dimensionality reduction is an important pre-processing step in many applications. Linear
discriminant analysis (LDA) is a classical statistical approach for supervised dimensionality …
discriminant analysis (LDA) is a classical statistical approach for supervised dimensionality …
Complexity-reduced implementations of complete and null-space-based linear discriminant analysis
GF Lu, W Zheng - Neural networks, 2013 - Elsevier
Dimensionality reduction has become an important data preprocessing step in a lot of
applications. Linear discriminant analysis (LDA) is one of the most well-known …
applications. Linear discriminant analysis (LDA) is one of the most well-known …
Local linear discriminant analysis framework using sample neighbors
The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …
The algorithms of LDA usually perform well under the following two assumptions. The first …