The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experiments show that it has a comparable performance with SVM
In this paper, we present an iterative approach to Fisher discriminant analysis called Kullback-Leib...
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We a...
Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it i...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality ...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension ...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
Nonlinear discriminant analysis may be transformed into the form of kernel-based discriminant analys...
Abstract—An alternative nonlinear multiclass discriminant al-gorithm is presented. This algorithm is...
Abstract—Recently the kernel discriminant analysis (KDA) has been successfully applied in many appli...
In this paper a novel incremental dimensionality reduction (DR) technique called incremental acceler...
Linear Discriminant Analysis (LDA) is a well-known method for fea-ture extraction and dimension redu...
In this paper, we present an iterative approach to Fisher discriminant analysis called Kullback-Leib...
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We a...
Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it i...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality ...
We study the use of kernel subspace methods that learn low-dimensional subspace representations for ...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension ...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
Nonlinear discriminant analysis may be transformed into the form of kernel-based discriminant analys...
Abstract—An alternative nonlinear multiclass discriminant al-gorithm is presented. This algorithm is...
Abstract—Recently the kernel discriminant analysis (KDA) has been successfully applied in many appli...
In this paper a novel incremental dimensionality reduction (DR) technique called incremental acceler...
Linear Discriminant Analysis (LDA) is a well-known method for fea-ture extraction and dimension redu...
In this paper, we present an iterative approach to Fisher discriminant analysis called Kullback-Leib...
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We a...
Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it i...
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