Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and spa...

This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analys...

Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-view face detection. Specifically, a recursive nonparametric discriminant analysis (RNDA) method is presented. The RNDA relaxes Gaussian assumptions of Fisher discriminant analysis (FDA), and it can handle more general ...

Fisher linear discriminant analysis (LDA) and its kernel extension— kernel discriminant analysis (KDA)—are well known methods that consider dimensionality reduction and classification jointly. While widely deployed in practical problems, there are still unresolved issues surrounding their efficient implementation and their relationship with least mean squared error procedures. In this paper we ...

Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-view face detection. Specifically, a recursive nonparametric discriminant analysis (RNDA) method is presented. The RNDA relaxes Gaussian assumptions of Fisher discriminant analysis (FDA), and it can handle more general ...

Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...

In this article, it was planned that we shall discuss Discriminant and Cluster analysis. While preparing the discussions for both topics, there was an overwhelming large amount of information and thus we shall concentrate on Discriminant analysis only and leave Cluster analysis to Biostatistics 304. Discriminant analysis (DA) was the traditional statistical technique used for differentiating gr...

Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several two-dimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA method, introduced by Ye et al. (2005), is an important development. The idea is to utilize the underlying 2D data ...

Linear Discriminant Analysis (LDA) and its nonlinear version Kernel Discriminant Analysis (KDA) are well-known and widely used techniques for supervised feature extraction and dimensionality reduction. They determine an optimal discriminant space for (non)linear data projection based on certain assumptions, e.g. on using normal distributions (either on the input or in the kernel space) for each...