Factor mixture models (FMM's) are latent variable models with categorical and continuous latent variables which can be used as a model-based approach to clustering. A previous paper covered the results of a simulation study showing that in the absence of model violations, it is usually possible to choose the correct model when fitting a series of models with different numbers of classes and fac...

BACKGROUND Several types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete...

Latent class analysis is used to perform model based clustering formultivariate categorical responses. Selection of the variables most relevant for clustering is an important task which can affect the quality of clustering considerably. This work considers a Bayesian approach for selecting the number of clusters and the best clustering variables. The main idea is to reformulate the problem of g...

This chapter gives an applied introduction to latent profile and class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals probing whether and, if so, how many classes can be identified the data estimating their proportional size response profiles. Moreover, membership serve as a predictor or outcome external variables. Substa...

Verbs were clustered semantically on the basis of their alternation behaviour, as characterised by their syntactic subcategorisation frames extracted from maximum probability parses of a robust statistical parser, and completed by assigning WordNet classes as selectional preferences to the frame arguments. The clustering was achieved (a) iteratively by measuring the relative entropy between the...

The non-linear decision boundary between object and background classes due to large intra-class variations needs to be modelled by any classifier wishing to achieve good results. While a mixture of linear classifiers is capable of modelling this non-linearity, learning this mixture from weakly annotated data is non-trivial and is the paper’s focus. Our approach is to identify the modes in the d...

This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (LM). We present an online clustering algorithm for LM based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In...

This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to traditional random utility specification aim of comparing two approaches on various measures including prediction accuracy and representation heterogeneity in choice process. Mixtur...