Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA). When there is no sufficien...

Abstract The accurate prediction for future tax values is the key to make the reasonable tax collection policy. In order to improve the prediction accuracy of wavelet support vector regression, linear discriminant analysis-wavelet support vector regression algorithm is proposed to predict future tax values in the paper. In the linear discriminant analysis-wavelet support vector regression algor...

Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-dimensional microarray data classification, due to the small number of samples and large number of features, classical LDA has sub-optimal performance corresponding to the singularity and instability of the within-group covariance matrix. Two modified LDA approaches (MLDA and NLDA) were applied for...

This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network ...

Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enha...

2 Classification of One-Dimensional Data 2 2.1 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.1 Building the LDA Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1.2 Results of One-Dimensional LDA Classification . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Quadratic Discriminant Analysis . . . . . ....

One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pairwise discriminant analysis (PDA) is proposed for pattern recognition. PDA, like linear discriminant analysis (LDA), performs dimensionality reduction and clustering, without suffering from undersampled data to the sam...

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA sha...

In this paper, we propose a new classification method using composite features, each of which consists of a number of primitive features. The covariance of two composite features contains information on statistical dependency among multiple primitive features. A new discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (...