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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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Proceedings Article
03 Dec 2007
TL;DR: It is demonstrated that LELVM not only provides sufficient constraints for robust operation in the presence of missing, noisy and ambiguous image measurements, but also compares favorably with alternative trackers based on PCA or GPLVM priors.
Abstract: Reliably recovering 3D human pose from monocular video requires models that bias the estimates towards typical human poses and motions. We construct priors for people tracking using the Laplacian Eigenmaps Latent Variable Model (LELVM). LELVM is a recently introduced probabilistic dimensionality reduction model that combines the advantages of latent variable models—a multimodal probability density for latent and observed variables, and globally differentiable nonlinear mappings for reconstruction and dimensionality reduction—with those of spectral manifold learning methods—no local optima, ability to unfold highly nonlinear manifolds, and good practical scaling to latent spaces of high dimension. LELVM is computationally efficient, simple to learn from sparse training data, and compatible with standard probabilistic trackers such as particle filters. We analyze the performance of a LELVM-based probabilistic sigma point mixture tracker in several real and synthetic human motion sequences and demonstrate that LELVM not only provides sufficient constraints for robust operation in the presence of missing, noisy and ambiguous image measurements, but also compares favorably with alternative trackers based on PCA or GPLVM priors.

66 citations

Journal ArticleDOI
TL;DR: The dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose and the linear dynamic system is introduced for incorporation of the dynamical data feature.
Abstract: Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression modeling purpose. Based on the probabilistic latent variable modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation–maximization Algorithm is introduced for parameter learning of the dynamical probabilistic latent variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic latent variable model.

65 citations

Book ChapterDOI
01 Jan 2001
TL;DR: Confirmatory Factor Analysis (CFA) is a mainly dis-confirmatory quantitative data analysis method that belongs to the family of structural equation modeling (SEM) techniques.
Abstract: Confirmatory factor analysis (CFA) is a mainly dis-confirmatory quantitative data analysis method that belongs to the family of structural equation modeling (SEM) techniques CFA allows for the assessment of fit between observed data and an a prioriconceptualized, theoretically grounded model that specifies the hypothesized causal relations between latent factors and their observed indicator variables In this article, typical steps in a CFA are introduced First, during model specification, a model is conceptualized by indicating how latent, unobserved factors relate to measurable variables Second, if each parameter can be expressed as a function of the variances and covariances of observed variables, model identification is assured and parameters can be estimated Third, iterative techniques such as the maximum likelihood, generalized least squares, or asymptotically distribution free estimation methods can be utilized to estimate the unknown model parameters Fourth, assessments of fit between observed data and the a priori specified model(s) can be made via a multitude of absolute, parsimonious, and incremental fit indices Fifth, if data-model inconsistencies are observed, model modifications might be appropriate, provided they are consistent with underlying substantive theories and the modified model is cross-validated using an independent sample The article closes with applied and methodological references appropriate for a more in-depth study of CFA and SEM in the social and behavioral sciences

65 citations


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