We introduce an efficient statistical modeling technique called Mixture of Principal Components (MPC). This model is a linear extension to the traditional Principal Component Analysis (PCA) and uses a mixture of eigenspaces to capture data variations. We use the model to capture face appearance variations due to pose and lighting changes. We show that this more efficient modeling leads to impro...

A method is proposed to incorporate mixture density splitting into the acoustic model discriminative training for speech recognition. The standard method is to obtain a high resolution acoustic model by maximum likelihood training and density splitting, and then improving this model by discriminative training. We choose a log-linear form of acoustic model because for a single Gaussian density p...

A linear latent growth curve mixture model is presented which includes switching between growth curves. Switching is accommodated by means of a Markov transition model. The model is formulated with switching as a highly constrained multivariate mixture model and is fitted using the freely available Mx program. The model is illustrated by analyzing data from the National Longitudinal Survey of Y...

a parallel hybrid system of hmm and gmm modeling techniques was implemented and used in a telephony speaker verification and identification system. spectral subtraction and weighted projection measure were used to render this system more robust against additional noise. cepstral mean subtraction method was also applied for the compensation of convolution noise due to transmission channel degrad...

We present a method for non-linear data projection that offers non-linear versions of Principal Component Analysis and Canonical Correlation Analysis. The data is accessed through a probabilistic mixture model only, therefore any mixture model for any type of data can be plugged in. Gaussian mixtures are one example, but mixtures of Bernoulli’s to model discrete data might be used as well. The ...

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous-discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different ...

Contents 1 Introduction 2 The R program 3 Phenology, an introduction 4 Geo-statistical modelling & analysis of phenological phases 5 Multivariate tools to visualise spatio-temporal patterns in phenology 6 Gaussian Mixture Models of geographical phenomena 7 Analysis of space-time correlations within geographical studies

We present a Switching Kalman Filter Model (SKFM) for the real-time inference of hand kinematics from a population of motor cortical neurons. First we model the probability of the firing rates of the population at a particular time instant as a Gaussian mixture where the mean of each Gaussian is some linear function of the hand kinematics. This mixture contains a “hidden state”, or weight, that...