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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.


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TL;DR: Deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks, is presented.
Abstract: We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks. We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time. We also propose a variant of VCCA called VCCA-private that can, in addition to the "common variables" underlying both views, extract the "private variables" within each view, and disentangles the shared and private information for multi-view data without hard supervision. Experimental results on real-world datasets show that our methods are competitive across domains.

109 citations

Journal ArticleDOI
TL;DR: In this article, a test battery of binary items is assumed to follow a Rasch model and the latent individual parameters are distributed within a given population in accordance with a normal distribution, and methods are then considered for estimating the mean and variance of this latent population distribution.
Abstract: Under consideration is a test battery of binary items. The responses ofn individuals are assumed to follow a Rasch model. It is further assumed that the latent individual parameters are distributed within a given population in accordance with a normal distribution. Methods are then considered for estimating the mean and variance of this latent population distribution. Also considered are methods for checking whether a normal population distribution fits the data. The developed methods are applied to data from an achievement test and from an attitude test.

109 citations

Journal ArticleDOI
TL;DR: In this article, a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity in gene expression in large cell populations is proposed.
Abstract: Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations.

109 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a method for variable selection that first estimates the regression function, yielding a "pre-conditioned" response variable, and then applies a standard procedure such as forward stepwise selection or the LASSO to the preconditioned response variable.
Abstract: We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function, yielding a "pre-conditioned" response variable. The primary method used for this initial regression is supervised principal components. Then we apply a standard procedure such as forward stepwise selection or the LASSO to the pre-conditioned response variable. In a number of simulated and real data examples, this two-step procedure outperforms forward stepwise selection or the usual LASSO (applied directly to the raw outcome). We also show that under a certain Gaussian latent variable model, application of the LASSO to the pre-conditioned response variable is consistent as the number of predictors and observations increases. Moreover, when the observational noise is rather large, the suggested procedure can give a more accurate estimate than LASSO. We illustrate our method on some real problems, including survival analysis with microarray data.

108 citations

Book ChapterDOI
01 Jan 2010
TL;DR: The multigroup latent class (LC) model as mentioned in this paper is an extension of the standard LC model for the analysis of latent structures of observed categorical variables across two or more groups.
Abstract: This chapter introduces the basic multigroup latent class (LC) model, and discusses two important extensions of the basic model, an extension for dealing with ordinal indicators and for modeling the latent variables as ordinal variables. It analyses measurement invariance using multigroup LC models, discussing the general procedure as well as methods for parameter estimation and evaluation of model fit. The multigroup extension of the standard LC model has been developed for the analysis of latent structures of observed categorical variables across two or more groups. The chapter presents three parameterizations of the multigroup LC models: probabilistic, log-linear, and logistic parameterizations. Multigroup LC models assume the presence of three types of categorical variables: observed variables; an unobserved variable that accounts for the relationships between the observed variables. LC models are usually estimated by means of maximum-likelihood under the assumption of a multinomial distribution for the indicator variables in the model.

108 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202375
2022143
2021137
2020185
2019142
2018159