This paper presents a speaker clustering framework by iteratively performing two stages: a discriminative feature space is obtained given a cluster label set, and the cluster label set is updated using a clustering algorithm given the feature space. In the iterations of two stages, the cluster labels may be different from the true labels, and thus the obtained feature space based on the labels ...

We present a novel probabilistic latent variable model to perform linear dimensional reduction on data sets which contain clusters. The model simultaneously performs the clustering and the dimensional reduction in an unsupervised manner, providing an optimal visualisation of the clusterig. We prove that the resulting dimensional reduction results in Fisher’s linear discriminant analysis as a li...

We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions. We focus on the setting where the covariance matrices for the two conditional distributions are the same. The corresponding generative model classifier, derived via the Bayes rule, also called Linear Discriminant Analysis, has been shown...

Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (observations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this problem is by using ...

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great att...

The proposed approach leads to analyzing the associations between a set of quantitative variables and several qualitative variables measured on a same set of individuals. In a decision-making context, the proposed method can be considered as a generalization of discriminant analysis to the multiple groups’ variables case. It’s described as a principal component analysis of the centres of gravit...

accurate estimate of flood flows at selected return periods is a fundamental step in flood control and design of hydraulic structures. due to the inadequacy or unavailability of flood flow records in several western catchments of iran, traditional regional flood frequency methods lead to remarkable estimate errors, particularly for long return periods. in this research, discriminant analysis is...

This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform...

background: unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. in the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population.   methods : in this cross-sectional study, 887 pregnant mothers referring to health centers in khorramabad, iran, in 2012 were selec...

A conceptual framework is presented for a unified treatment of issues arising in a variety of predictability studies. The predictive power (PP), a predictability measure based on information–theoretical principles, lies at the center of this framework. The PP is invariant under linear coordinate transformations and applies to multivariate predictions irrespective of assumptions about the probab...