Linear Discriminant Analysis (LDA) is widely used for feature extraction in face recognition but suffers from small sample size (SSS) problem in its original formulation. Exponential discriminant analysis (EDA) is one of the variants of LDA suggested recently to overcome this problem. For many real time systems, it may not be feasible to have all the data samples in advance before the actual mo...

Dimensionality reduction technique is applied to get rid of the inessential terms like redundant and noisy terms in documents. In this paper a systematic study is conducted for seven dimensionality reduction methods such as Latent Semantic Indexing (LSI), Random Projection (RP), Principle Component Analysis (PCA) and CUR decomposition, Latent Dirichlet Allocation(LDA), Singular value decomposit...

Phenolic compounds in 46 Spanish cider apple varieties were determined by RP-HPLC with direct injection. Several pattern recognition procedures, including principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares (PLS-1), were applied to the data in an attempt to classify the samples into bitter and nonbitter categories. Reliable decision rules were obtai...

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision boundary by means of a fuzzy linear programming approach with fuzzy resources. The method proposed has low computational complexity because of its linear character...

In this paper, a new hybrid classifier is proposed by combining neural network and direct fractional-linear discriminant analysis (DF-LDA). The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. The weakly performing perceptron nodes are replaced with DF-LDA i...

Linear Discriminant Analysis (LDA) is among the most optimal dimension reduction methods for classification, which provides a high degree of class separability for numerous applications from science and engineering. However, problems arise with this classical method when one or both of the scatter matrices is singular. Singular scatter matrices are not unusual in many applications, especially f...

To improve speech recognition performance, feature transformation based on discriminant analysis has been widely used to reduce the redundant dimensions of acoustic features. Linear discriminant analysis (LDA) and heteroscedastic discriminant analysis (HDA) are often used for this purpose, and a generalization method for LDA and HDA, called power LDA (PLDA), has been proposed. However, these me...

We consider the supervised classification setting, in which the data consist of p features measured on n observations, each of which belongs to one of K classes. Linear discriminant analysis (LDA) is a classical method for this problem. However, in the high-dimensional setting where p ≫ n, LDA is not appropriate for two reasons. First, the standard estimate for the within-class covariance matri...

The linear discriminant analysis based on the generalized singular value decomposition (LDA/GSVD) has recently been introduced to circumvents the nonsingularity restriction that occur in the classical LDA so that a dimension reducing transformation can be effectively obtained for undersampled problems. In this paper, relationships between support vector machines (SVMs) and the generalized linea...

Linear discriminant analysis (LDA) is a simple but widely used algorithm in pattern recognition. However it has some shortcomings in that it is sensitive to outliers and limited to linearly separable cases. To solve this problem a new version of nonlinear discriminant algorithm is proposed. This new version, SC-LLE, uses LDA combined with LLE method to take into account non-linearly separable c...