Fisher Discriminant Analysis (FDA) is a powerful and popular method for dimensionality reduction and classification which has unfortunately poor performances in the cases of label noise and sparse labeled data. To overcome these limitations, we propose a probabilistic framework for FDA and extend it to the semi-supervised case. Experiments on realworld datasets show that the proposed approach w...

Despite its age, the Linear Discriminant Analysis performs well even in situations where the underlying premises like normally distributed data with constant covariance matrices over all classes are not met. It is, however, a global technique that does not regard the nature of an individual observation to be classified. By weighting each training observation according to its distance to the obs...

Classi cation in high-dimensional feature spaces where interpretation and dimension reduction are of great importance is common in biological and medical applications. For these applications standard methods such as microarrays, 1D NMR, and spectroscopy have become everyday tools for measuring thousands of features in samples of interest. The samples are often costly and therefore many problems...

Barycentric discriminant analysis (badia) generalizes discriminant analysis and, like discriminant analysis, it is performed when measurements made on some observations are combined to assign these observations or “new” observations to a-priori defined categories. For example, badia can be used 1) to assign subjects to a given diagnostic group (i.e., Alzheimer’s disease, other dementia, normal ...

In this paper, a novel nonlinear discriminant analysis is proposed. Experimental results show that the new method provides state of the art performance when combined with LSVM in terms of training time and accuracy.

As thename indicates, discriminant correspondence analysis (DCA) is an extension of discriminant analysis (DA) and correspondence analysis (CA). Like discriminant analysis, the goal of DCA is to categorize observations in pre-defined groups, and like correspondence analysis, it is used with nominal variables. The main idea behind DCA is to represent each group by the sum of its observations and...

Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in...

the problem of discrimination between two stationary ar(p) plus noise processes is consideredwhen the noise process are different in two models. the discrimination rule leads to a quadratic form withcumbersome matrices. an approximate and analytic form is given to distribution of the discriminant. thesimulation study has been used to show the performance of discrimination rule. the cumulants of...