Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.

poLCA: An R Package for Polytomous Variable Latent Class Analysis

Abstract: poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

Beyond sem: general latent variable modeling

Abstract: This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random effects, missing data, sources of variation in hierarchical data, finite mixtures. latent classes, and clusters. These latent variable applications go beyond the traditional latent variable useage in psychometrics with its focus on measurement error and hypothetical constructs measured by multiple indicators. The article argues for the value of integrating statistical and psychometric modeling ideas. Different applications are discussed in a unifying framework that brings together in one general model such different analysis types as factor models, growth curve models, multilevel models, latent class models and discrete-time survival models. Several possible combinations and extensions of these models are made clear due to the unifying framework.

Supervised Learning of Semantic Classes for Image Annotation and Retrieval

Abstract: A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning

Automatic labeling of semantic roles

Abstract: We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data.

Latent class and latent transition analysis.

Abstract: Often quantities of interest in psychology cannot be observed directly. These unobservable quantities are known as latent variables. By using multiple items as indicators of the latent variable, we can obtain a more complete picture of the construct of interest and estimate measurement error. One approach to latent variable modeling is latent class analysis, a method appropriate for examining the relationship between discrete observed variables and a discrete latent variable. The present chapter will introduce latent class analysis, its extension to repeated measures, and recent developments further extending the latent class model. First, the concept of a latent class and the mathematical model are presented. This is followed by a discussion of parameter restrictions, model fit, and the measurement quality of categorical items. Second, latent class analysis is demonstrated through an examination of the prevalence of depression types in adolescents. Third, longitudinal extensions of the latent class model are presented. This section also contains an empirical example on adolescent depression types, where the previous analysis is extended to examine the stability and change in depression types over time. Finally, several recent developments that further extend the latent class model are introduced. Keywords: categorical variables; depression types; latent class analysis; latent transition analysis; latent variables; longitudinal