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Approximation and Inference using Latent Variable Sparse Linear Models
Published on Feb 01, 20084419 Views
A variety of Bayesian methods have recently been introduced for performing approximate inference using linear models with sparse priors. We focus on four methods that capitalize on latent structure in
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Chapter list
Approximation and Inference Using Latent Variable Sparse Linear Models00:00
Overview00:12
Sparse Linear Model02:07
Sparse Prior: 2D Example03:14
Practical Issues03:38
Latent Variable Models of Sparse Priors04:17
1d Example of Convex-Type Representation05:48
Four Possibilities for Approximation06:18
Method I: w-MAP07:19
Method II: g-MAP08:11
Method III: Convex Bounding09:55
Method IV: Variational Bayes11:09
Unification12:53
Unification Cont.14:39
Choosing a Model15:22
Optimization Issues17:12
Example Applications18:39
Maximally Sparse Representations19:18
Example20:35
Using g-MAP to Find w021:02
Two Criteria for Choosing f(g) - 121:39
Two Criteria for Choosing f(g) - 222:25
Result22:51
Associated ‘Full’ Model25:27
Notes about SBL (and RVMs)26:47
Experimental Design28:14
Problem29:47
One Heuristic Solution30:53
Extensions31:43
Non-Negative Sparse Coding31:55
Non-Negative Sparse Coding Cont.32:19
Empirical Example - 133:04
Empirical Example - 233:34
Classification34:01
Covariance Component Estimation34:01
Final Thoughts34:02
Thank You35:14