<p>A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images.</p>Thesi
A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc a...
Abstract—Factor analysis provides linear factors that describe relation-ships between individual var...
We develop a general approach to factor analysis that involves observed and latent variables that ar...
Factor analysis is a widely used method for modeling a set of observed variables by a set of unobser...
This work is explores linear dimensionality reduction techniques that preserve information relevant ...
Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of ...
<p>Tree structured graphical models are powerful at expressing long range or hierarchical dependency...
Real world data is not random: The variability in the data-sets that arise in computer vision, sign...
Latent factor models (LFMs) are a set of unsupervised methods that model observed high-dimensional d...
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{...
We present an integrated approach to structure and parameter estimation in latent tree graphical mod...
A very general class of multilevel factor analysis and structural equation models is proposed which ...
Exploratory factor analysis is a dimension-reduction technique commonly used in psychology, finance,...
Multinomial processing tree model played an important role in human information processing. A method...
Hierarchical multinomial models 2 Multinomial processing tree models are widely used in many areas o...
A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc a...
Abstract—Factor analysis provides linear factors that describe relation-ships between individual var...
We develop a general approach to factor analysis that involves observed and latent variables that ar...
Factor analysis is a widely used method for modeling a set of observed variables by a set of unobser...
This work is explores linear dimensionality reduction techniques that preserve information relevant ...
Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of ...
<p>Tree structured graphical models are powerful at expressing long range or hierarchical dependency...
Real world data is not random: The variability in the data-sets that arise in computer vision, sign...
Latent factor models (LFMs) are a set of unsupervised methods that model observed high-dimensional d...
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{...
We present an integrated approach to structure and parameter estimation in latent tree graphical mod...
A very general class of multilevel factor analysis and structural equation models is proposed which ...
Exploratory factor analysis is a dimension-reduction technique commonly used in psychology, finance,...
Multinomial processing tree model played an important role in human information processing. A method...
Hierarchical multinomial models 2 Multinomial processing tree models are widely used in many areas o...
A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc a...
Abstract—Factor analysis provides linear factors that describe relation-ships between individual var...
We develop a general approach to factor analysis that involves observed and latent variables that ar...