We describe a method for sparse feature selection for a class of problems motivated by our work in Computer-Aided Detection (CAD) systems for identifying structures of interest in medical images. Typical CAD data sets for classification are large (several thousand candidates) and unbalanced (significantly fewer than 1% of the candidates are ”positive”). To be accepted by physicians, CAD systems...

There are two fundamental problems of the Kernel Fisher Discriminant Analysis (KFDA) for nonlinear fault diagnosis. The first one is the classification performance of KFDA between the normal data and fault data degenerates as long as overlapping samples exist. The second one is that the computational cost of kernel matrix becomes large when the training sample number increases. Aiming at the tw...

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extract...

Abstract: Discriminant analysis for multiple groups is often done using Fisher’s rule, and can be used to classify observations into different populations. In this paper, we measure the performance of classical and robust Fisher discriminant analysis using the Error Rate as a performance criterion. We were able to derive an expression for the optimal error rate in the situation of three groups....

Fisher linear discriminant analysis (FDA) 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 squares procedures. In this paper we address...

In this paper, a new statistical projection-based method called Two-DimensionalOriented Linear Discriminant Analysis (2DO-LDA) is presented. While in the Fisherfaces method the 2D image matrices are first transformed into 1D vectors by merging their rows of pixels, 2DO-LDA is directly applied on matrices, as 2D-PCA. Within and between-class image covariance matrices are generalized, and 2DO-LDA...

Fisher linear discriminant analysis (FLDA) $nds a set of optimal discriminating vectors by maximizing Fisher criterion, i.e., the ratio of the between scatter to the within scatter. One of its major disadvantages is that the number of its discriminating vectors capable to be found is bounded from above by C-1 for C-class problem. In this paper for binary-class problem, we propose alternative FL...

A new palmprint recognition method based on local Fisher discriminant analysis(LFDA) is proposed. In order to solve the singularity of the eigenvalue equation matrix in small-size-sample cases such as image recognition, image down-sample is first used to reduce the palmprint space dimensionality. The LFDA is applied to extract the low projection vectors. Then the training images and test images...