Nonnegative matrix factorization NMF is a popular tool for analyzing the latent structure of nonnegative data. For a positive pairwise similarity matrix, symmetric NMF SNMF and weighted NMF WNMF can be used to cluster the data. However, both of them are not very efficient for the ill-structured pairwise similarity matrix. In this paper, a novel model, called relationship matrix nonnegative deco...

The uniformly minimum variance unbiased (UMVU), maximum likelihood, percentile (PC), least squares (LS) and weighted least squares (WLS) estimators of the probability density function (pdf) and cumulative distribution function are derived for the generalized Rayleigh distribution. This model can be used quite effectively in modelling strength data and also modeling general lifetime data. It has...

ÐWe derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a dif...

We propose an uncorrelated heteroscedastic LDA (UHLDA) technique, which extends the uncorrelated LDA (ULDA) technique by integrating the weighted pairwise Chernoff criterion. The UHLDA can extract discriminatory information present in both the differences between per class means and the differences between per class covariance matrices. Meanwhile, the extracted feature components are statistica...

Linear Discriminant Analysis (LDA) aims to transform an original feature space to a lower dimensional space with as little loss in discrimination as possible. We introduce a novel LDA matrix computation that incorporates confusability information between classes into the transform. Our goal is to improve discrimination in LDA. In conventional LDA, a between class covariance matrix that is based...

Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and tractability. However, this assumption seems be restrictive when dealing counting data. To deal situation, we propose a Poisson marginal distribution considering sequ...

A tournament is a binary dominance relation on a set of alternatives. Tournaments arise in many contexts that are relevant to AI, most notably in voting (as a method to aggregate the preferences of agents). There are many works that deal with choice rules that select a desirable alternative from a tournament, but very few of them deal directly with incentive issues, despite the fact that gameth...