Add to ORKGAbstract. Linear Discriminant (LD) techniques are typically used in pattern recognition tasks when there are many (n>> 10 4) datapoints in low-dimensional (d < 10 2) space. In this paper we argue on theoretical grounds that LD is in fact more appropriate when training data is sparse, and the dimension of the space is extremely high. To support this conclusion we present experimental results on a medical text classification problem of great practical importance, autocoding of adverse event reports. We trained and tested LD-based systems for a variety of classification schemes widely used in the clinical drug trial process (COSTART, WHOART, HARTS, and MedDRA) and obtained significant reduction in the rate of misclassification compare...
Many high dimensional classification techniques have been proposed in the litera-ture based on spars...
International audienceLinear Discriminant Analysis (LDA) is a technique which is frequently used to ...
Huge amount of applications in various fields, such as gene expression analysis or computer vision, ...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
Abstract — In the so-called high dimensional, low sample size (HDLSS) settings, LDA possesses the “d...
The proliferation of online platforms recently has led to unprecedented increase in data generation;...
International audienceIn this paper, we deal with the issue of classifying normally distributed data...
Linear discriminant analysis (LDA) is an important conventional model for data classification. Class...
Linear discriminant analysis has gained extensive applications in supervised classification and dime...
Dimensionality reduction is an important aspect in the pattern classification literature, and linear...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in classification ...
Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extr...
Abstract—High-dimensional data are common in many do-mains, and dimensionality reduction is the key ...
This paper studies high-dimensional linear discriminant analysis (LDA). First, we review the l(1) pe...
AbstractThe pragmatic realism of the high dimensionality incurs limitations in many pattern recognit...
Many high dimensional classification techniques have been proposed in the litera-ture based on spars...
International audienceLinear Discriminant Analysis (LDA) is a technique which is frequently used to ...
Huge amount of applications in various fields, such as gene expression analysis or computer vision, ...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
Abstract — In the so-called high dimensional, low sample size (HDLSS) settings, LDA possesses the “d...
The proliferation of online platforms recently has led to unprecedented increase in data generation;...
International audienceIn this paper, we deal with the issue of classifying normally distributed data...
Linear discriminant analysis (LDA) is an important conventional model for data classification. Class...
Linear discriminant analysis has gained extensive applications in supervised classification and dime...
Dimensionality reduction is an important aspect in the pattern classification literature, and linear...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in classification ...
Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extr...
Abstract—High-dimensional data are common in many do-mains, and dimensionality reduction is the key ...
This paper studies high-dimensional linear discriminant analysis (LDA). First, we review the l(1) pe...
AbstractThe pragmatic realism of the high dimensionality incurs limitations in many pattern recognit...
Many high dimensional classification techniques have been proposed in the litera-ture based on spars...
International audienceLinear Discriminant Analysis (LDA) is a technique which is frequently used to ...
Huge amount of applications in various fields, such as gene expression analysis or computer vision, ...