scispace - formally typeset
Search or ask a question
Topic

Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.


Papers
More filters
Proceedings ArticleDOI
09 Feb 2011
TL;DR: Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.
Abstract: This paper presents a topic model that identifies interpretable low dimensional components in time-stamped data for capturing the evolution of trends. Unlike other models for time-stamped data, our proposal, the trend analysis model (TAM), focuses on the difference between temporal words and other words in each document to detect topic evolution over time. TAM introduces a latent trend class variable into each document and a latent switch variable into each token for handling these differences. The trend class has a probability distribution over temporal words, topics, and a continuous distribution over time, where each topic is responsible for generating words. The latter class uses a document specific probabilistic distribution to judge which variable each word comes from for generating words in each token. Accordingly, TAM can explain which topic co-occurrence pattern will appear at any given time, and represents documents of similar content and timestamp as sharing the same trend class. Therefore, TAM projects them on a latent space of trend dimensionality and allows us to predict the temporal evolution of words and topics in document collections. Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.

68 citations

Journal ArticleDOI
TL;DR: In this paper, a terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented.
Abstract: A terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented. The strategy rejects persistent process disturbances and achieves new final product quality targets using an iterative procedure that works in the reduced space of a latent variable model rather than in the high dimensional space of the manipulated variable trajectories. Complete manipulated variable trajectory reconstruction is then achieved by exploiting the PLS model of the process. The approach is illustrated with a condensation polymerization example for the production of nylon.

68 citations

Proceedings ArticleDOI
12 May 2008
TL;DR: A technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality from a probabilistic latent variable model with sparsity constraints is described.
Abstract: In this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our approach employs a probabilistic latent variable model with sparsity constraints. We demonstrate its utility by performing feature extraction in a variety of domains ranging from audio to images and video.

68 citations

Journal ArticleDOI
TL;DR: In this paper, a maximum likelihood approach for analyzing a latent variable model with two-level data is proposed, which involves the Gibbs sampler for approximating the E-step and the M-step, and the bridge sampling for monitoring the convergence.
Abstract: Summary. Two-level data with hierarchical structure and mixed continuous and polytomous data are very common in biomedical research. In this article, we propose a maximum likelihood approach for analyzing a latent variable model with these data. The maximum likelihood estimates are obtained by a Monte Carlo EM algorithm that involves the Gibbs sampler for approximating the E-step and the M-step and the bridge sampling for monitoring the convergence. The approach is illustrated by a two-level data set concerning the development and preliminary findings from an AIDS preventative intervention for Filipina commercial sex workers where the relationship between some latent quantities is investigated.

68 citations

Book ChapterDOI
01 Jan 1988
TL;DR: Extensions and modifications of latent class models reported below are intended to remove a deficiency in latent class analysis that deals in a direct way with measurement.
Abstract: Most latent class analysis in contemporary social research is aimed at data reduction or “building clusters for qualitative data” (Formann, 1985, p. 87; see also Aitkin, Anderson, & Hinde, 1981). Some special restricted models in this area have of course been used to represent structural characteristics or behavioral processes (e.g., Clogg, 1981a; Goodman, 1974a). But a careful examination of the latent class models now available shows that none deal in a direct way with measurement, particularly if exacting standards are used to define how measurement should take place. Extensions and modifications of latent class models reported below are intended to remove this deficiency.

67 citations


Network Information
Related Topics (5)
Statistical hypothesis testing
19.5K papers, 1M citations
82% related
Inference
36.8K papers, 1.3M citations
81% related
Multivariate statistics
18.4K papers, 1M citations
80% related
Linear model
19K papers, 1M citations
80% related
Estimator
97.3K papers, 2.6M citations
78% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202375
2022143
2021137
2020185
2019142
2018159