A generalized linear discriminant analysis based on trace ratio criterion algorithm (GLDA-TRA) is derived to extract features for classification. With the proposed GLDA-TRA, a set of orthogonal features can be extracted in succession. Each newly extracted feature is the optimal feature that maximizes the trace ratio criterion function in the subspace orthogonal to the space spanned by the previous extracted features
We present an enhanced direct linear discriminant analysis (EDLDA) solution to effectively and effic...
This study investigates the area of feature extraction for statistical pattern recognition. The redu...
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction an...
Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which ai...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA), ...
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rig...
A generalized discriminant analysis based on a new optimization criterion is presented. The criterio...
A new feature extraction criterion, maximum margin criterion (MMC), is proposed in this paper. This ...
Generalized linear discriminant analysis has been successfully used as a dimensionality reduction te...
Abstract — In this brief, we address the trace ratio (TR) problem for semi-supervised dimension redu...
In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We f...
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...
Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern ...
We present an enhanced direct linear discriminant analysis (EDLDA) solution to effectively and effic...
This study investigates the area of feature extraction for statistical pattern recognition. The redu...
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction an...
Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which ai...
Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear tra...
This work studies the theoretical rules of feature selection in linear discriminant analysis (LDA), ...
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rig...
A generalized discriminant analysis based on a new optimization criterion is presented. The criterio...
A new feature extraction criterion, maximum margin criterion (MMC), is proposed in this paper. This ...
Generalized linear discriminant analysis has been successfully used as a dimensionality reduction te...
Abstract — In this brief, we address the trace ratio (TR) problem for semi-supervised dimension redu...
In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We f...
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...
Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern ...
We present an enhanced direct linear discriminant analysis (EDLDA) solution to effectively and effic...
This study investigates the area of feature extraction for statistical pattern recognition. The redu...
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction an...