Add to ORKGLinear discriminant analysis (LDA) is one of the most popular dimension reduction meth-ods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experi-mental results demonstrate that by considering multi-labeled structure, LDA can achieve computational efficiency and also improve classification performances
Abstract—Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which proj...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
In this paper, we study the relationship between Linear Discriminant Analysis (LDA) and the generali...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
In this paper, we study the relationship between Linear Discriminant Analysis(LDA) and the generaliz...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
The Fisher linear discriminant analysis (LDA) is a classical method for classification and dimen-sio...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
A generalized discriminant analysis based on a new optimization criterion is presented. The criterio...
Abstract—Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which proj...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
In this paper, we study the relationship between Linear Discriminant Analysis (LDA) and the generali...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
In this paper, we study the relationship between Linear Discriminant Analysis(LDA) and the generaliz...
Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a met...
The Fisher linear discriminant analysis (LDA) is a classical method for classification and dimen-sio...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. How...
A generalized discriminant analysis based on a new optimization criterion is presented. The criterio...
Abstract—Linear discriminant analysis (LDA) is a well-known dimension reduction approach, which proj...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Dimensionality reduction is an important issue when facing high-dimensional data. For supervised dim...