Bayesian learning of sparse factor loadings
author: Magnus Rattray,
School of Mathematics, University of Manchester
published: Oct. 9, 2008, recorded: September 2008, views: 5279
published: Oct. 9, 2008, recorded: September 2008, views: 5279
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
Learning sparse structure is useful in many applications. For example, gene regulatory networks are sparsely connected since each gene is typically only regulated by a small number of other genes. In this case factor analysis models with sparse loading matrices have been used to uncover the regulatory network from gene expression data. In this talk I will examine the performance of sparsity priors, such as mixture and L1 priors, by calculating learning curves for Bayesian PCA in the limit of large data dimension. This allows us to address a number of questions e.g. how well can we estimate sparsity using the marginal likelihood when the prior is not well-matched to the data generating process?
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: