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several practical problems of hypotheses testing can be under a general linear model analysis of variance which would be examined. in analysis of variance, when the response random variable y , has linear relationship with several random variables x, another important model as analysis of covariance can be used. in this paper, assuming that y is fuzzy and using dlr metric, a method for testing ...

In this paper, a modified Fisher linear discriminant analysis (FLDA) is proposed and aims to not only overcome the rank limitation of FLDA, that is, at most only finding a discriminant vector for 2-class problem based on Fisher discriminant criterion, but also relax singularity of the within-class scatter matrix and finally improves classification performance of FLDA. Experiments on nine public...

We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension...

this paper describes a study of operational parameters by using the multivariate data analysis and neural networks for a municipal waste incinerator located in majorca (spain). the basis of the study also includes the chemometric techniques: linear multivariate regression to develop a model with certain predictive capabilities; linear principal component analysis, which allow the number of vari...

We propose new algorithms for computing linear discriminants to perform data dimensionality reduction from R to R, with p < n. We propose alternatives to the classical Fisher’s Distance criterion, namely, we investigate new criterions based on the: Chernoff-Distance, J-Divergence and Kullback-Leibler Divergence. The optimization problems that emerge of using these alternative criteria are non-c...

The objects in this course are infinite dimensional vector spaces (hence the term “linear”) over R or C, together with additional structure (a “norm” or “inner product”) which “respects” in some way the linear structure. This additional structure will allow us to do “analysis”. The most pedestrian way to understand the last sentence is that it will allow us to “take limits”. In fact, the extra ...

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...