The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated and bench-marked on artificial and real biological data sets.
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Abstract. In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of ...
This paper proposes a computationally efficient method for joint parameter and model inference, and model comparison, and proposes a fully Bayesian ...
In this paper we consider sparse and identifiable linear latent variable(factor) and linear Bayesian network models for parsimonious analysis ofmultivariate ...
Title. Sparse Linear Identifiable Multivariate Modeling. Authors. Henao, Ricardo; Winther, Ole. Publication. Journal of Machine Learning Research, 2011, ...
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of ...
Sparse Linear Identifiable Multivariate Modeling Ole Winther and Ricardo Henao ; Journal: Journal of Machine Learning Research, ; Volume: 12 ; URL: http://www.jmlr ...
This thesis presents a collection of statistical models that attempt to take ad- vantage of every piece of prior knowledge available to provide the models ...
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response ...
Nov 1, 2018 · In this paper, we present a comprehensive framework for simultaneous sparse model identification and learning for ultra-high-dimensional APLMs.