Add to ORKGLatent factor models (LFMs) are a set of unsupervised methods that model observed high-dimensional data examples by linear combination of latent factors. To enable efficient pro-cessing of large data collections, LFMs aim to find concise descriptions of the members of a data collection while preserving the essential statistical information which is useful for basi
Latent structure models involve real, potentially observable variables and latent, unobservable vari...
<p>Using an empirical data set, we investigated variation in factor model parameters across a contin...
One central task in machine learning (ML) is to extract hidden knowledge and structure from observed...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor a...
Latent class (LC) analysis is becoming one of the standard data analysis tools in social, biomedical...
In this paper, we study latent factor models with dependency structure in the la-tent space. We prop...
What is a latent variable? Simply defined, a latent variable is a variable that cannot be directly m...
Abstract—Factor analysis provides linear factors that describe relation-ships between individual var...
Real world data is not random: The variability in the data-sets that arise in computer vision, sign...
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces...
<p>A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a ...
A major goal of data mining is to extract a relatively small number of meaningful “factors ” from a ...
Logical Factorisation Machines (LFMs) are a class of latent feature models, that aim to decompose bi...
Latent factor models are widely used to measure unobserved latent traits in so- cial and behavioral ...
Latent structure models involve real, potentially observable variables and latent, unobservable vari...
<p>Using an empirical data set, we investigated variation in factor model parameters across a contin...
One central task in machine learning (ML) is to extract hidden knowledge and structure from observed...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor a...
Latent class (LC) analysis is becoming one of the standard data analysis tools in social, biomedical...
In this paper, we study latent factor models with dependency structure in the la-tent space. We prop...
What is a latent variable? Simply defined, a latent variable is a variable that cannot be directly m...
Abstract—Factor analysis provides linear factors that describe relation-ships between individual var...
Real world data is not random: The variability in the data-sets that arise in computer vision, sign...
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces...
<p>A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a ...
A major goal of data mining is to extract a relatively small number of meaningful “factors ” from a ...
Logical Factorisation Machines (LFMs) are a class of latent feature models, that aim to decompose bi...
Latent factor models are widely used to measure unobserved latent traits in so- cial and behavioral ...
Latent structure models involve real, potentially observable variables and latent, unobservable vari...
<p>Using an empirical data set, we investigated variation in factor model parameters across a contin...
One central task in machine learning (ML) is to extract hidden knowledge and structure from observed...