linear latent variable models
Learning the Structure of Linear Latent Variable Models
... to latent vari- ables (Spirtes et ...indicator models” in which multiple observed measures are assumed to be effects of unrecorded variables and possibly of each other − machine learning has provided no ...
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Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data
... In this article, we define the Bayesian estimate of as the mean of the posterior distribution (called the posterior mean). For simple structural equation models, the posterior mean can be obtained through direct ...
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Evaluating latent variable interactions with structural equation mixture models
... generating models the SEMM approach provided the least biased approximation in the main effects ...uses linear SEMs to approximate a linear ...interaction models (see Table 4, 6 and ...
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Building Blocks for Variational Bayesian Learning of Latent Variable Models
... to linear factor analysis, independent component analysis (ICA) (Hyv ¨arinen et ...include linear independent factor analysis (Attias, 1999), several other extensions of the ba- sic linear ICA model ...
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Linear Latent Force Models Using Gaussian Processes
... the latent force model using as an example a latent variable ...order latent force model to describe how the general framework can be used in ...a latent force model based on a second ...
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Penalized Latent Variable Estimator For Finite Mixture of Regression Models.
... Clusterwise linear regression techniques can be categorized by two main approaches, algorithmic and likelihood approaches. For context, the term algorithmic implies a variation of a semi-exhaustive combinatorial ...
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A Comparison of Latent Variable Models For Conversation Analysis
... For the task of identifying the speaker who uttered the turn, we employ an approach, similar to the one used for ATM. We train speaker-addressee-specific models. The feature set for this task includes fea- tures ...
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Nonconvex Global Optimization for Latent Variable Models
... We caution that the linear relaxations are very loose on larger boxes. Since we have many dimen- sions, the binary branch-and-bound tree may have to grow quite deep before the boxes become small enough to prune. ...
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SILVar: Single Index Latent Variable Models
... In Figure 2, we plot the original time series, estimated trends, the estimated network, and residuals. The plots are all from time steps 3 to 363, since those are the indices for the trends that we can reliably estimate ...
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Discriminative latent variable models for visual recognition
... that latent SVM (LSVM) is a class of powerful tools that have been successfully applied to many applications in computer ...of latent SVM is that it relies on linear models ...tasks, ...
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Latent Variable Generative Models for Data Efficient Text Classification
... that linear generative classi- fiers approach their asymptotic error rates more rapidly than discriminative ...swering models (Lewis and Fan, 2019) demon- strates better robustness to biased training data ...
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Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
... Visualisation of high dimensional data can be achieved through projecting a data-set onto a lower dimensional manifold. Linear projections have traditionally been preferred due to the ease with which they can be ...
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Online Tensor Methods for Learning Latent Variable Models
... is linear algebra based, it is not surprising that the standard linear algebraic solver libraries like BLAS and Linear Algebra PACKage (LAPACK) also have their equivalents for Nvidia GPUs in one form ...
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Learning Linear Cyclic Causal Models with Latent Variables
... In this section, we present an algorithm, termed LLC, for inferring a linear cyclic model with latent variables, provided finite sample data from a set of experiments over the given variable set. ...
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Feature Rich Log Linear Lexical Model for Latent Variable PCFG Grammars
... with latent anno- tations (PCFG-LA) have been found to be effective for parsing many languages; however, currently their lexical model may be subject to over-fitting and requires language engineering to handle ...
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Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
... probabilistic latent variable ...the latent projections, X, to the observed data, ...the latent-space and the latent-space’s posterior distribution is approximated by ...the ...
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Variable selection in partially linear wavelet models.
... of variable selection for the parametric part of the ...on variable selection for linear models, little work has been done for variable selection in partially linear ...
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Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
... In order to justify the usefulness of EP for Ising models we therefore need an alternative argu- ment. Our argument is entirely restricted to Gaussian EP for our extended definition of GLVMs and do not extend to ...
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Latent mixed models
... The linear mixed model has been a major research interest of Dr Arthur Gilmour, motivated by problems arising in research data generated by agricultural ...the linear mixed ...of linear mixed ...
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Language as a Latent Variable: Discrete Generative Models for Sentence Compression
... Auto-encoders (Rumelhart et al., 1985) are a typi- cal neural network architecture for learning compact data representations, with the general aim of perform- ing dimensionality reduction on embeddings (Hinton and ...
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