Add to ORKGLinear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a method called Generalized discriminant analysis (GDA) has been developed to deal with nonlinear discriminant analysis using kernel functions. Difficulties for GDA method can arise both in the form of computational complexity and storage requirements. In this paper, we present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints. 1
Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popula...
Abstract. Linear Discriminant Analysis (LDA) has been widely used for linear dimension reduction. Ho...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
In order to overcome the computation and storage prob-lem for large-scale data set, an efficient ite...
Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it i...
Nonlinear discriminant analysis may be transformed into the form of kernel-based discriminant analys...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
Abstract—An alternative nonlinear multiclass discriminant al-gorithm is presented. This algorithm is...
Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension ...
An alternative nonlinear multiclass discriminant algorithm is presented.This algorithm is based on t...
The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) ...
Abstract—Linear and kernel discriminant analyses are popular approaches for supervised dimensionalit...
Generalized discriminant analysis (GDA) is a commonly used method for dimensionality reduction. In i...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popula...
Abstract. Linear Discriminant Analysis (LDA) has been widely used for linear dimension reduction. Ho...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
In order to overcome the computation and storage prob-lem for large-scale data set, an efficient ite...
Linear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it i...
Nonlinear discriminant analysis may be transformed into the form of kernel-based discriminant analys...
In Linear Discriminant Analysis (LDA), a dimension reducing linear transformation is found in order...
Abstract—An alternative nonlinear multiclass discriminant al-gorithm is presented. This algorithm is...
Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension ...
An alternative nonlinear multiclass discriminant algorithm is presented.This algorithm is based on t...
The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) ...
Abstract—Linear and kernel discriminant analyses are popular approaches for supervised dimensionalit...
Generalized discriminant analysis (GDA) is a commonly used method for dimensionality reduction. In i...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popula...
Abstract. Linear Discriminant Analysis (LDA) has been widely used for linear dimension reduction. Ho...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...