Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal str...
Haesun Park, Jaegul Choo, Barry L. Drake, Jinwoo K...
Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become signi...
Linear discriminant analysis (LDA) is a widely used feature extraction method for classification. We introduce distributed implementations of different versions of LDA, suitable ...
Sergio Valcarcel Macua, Pavle Belanovic, Santiago ...
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear re...
Abstract. Linear Discriminant (LD) techniques are typically used in pattern recognition tasks when there are many (n >> 104 ) datapoints in low-dimensional (d < 102 ) spac...