Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop

Greedy Feature Grouping for Optimal Discriminant Subspaces

author: Mahesan Niranjan, Department of Molecular Biology and Biotechnology, University of Sheffield

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*Slides*
0:08	Greedy Feature Grouping for Optimal Discriminant Subspaces
0:50	Overview
1:27	Motivation
2:51	Curse of dimensionality
4:20	Support Vector Machines
4:48	Support Vector Machines Nonlinear Kernel Functions
4:51	Classifier design
5:38	Adverse Outcome
6:46	True Positive
8:35	Convex Hull of ROC Curves
10:12	Feature selection in classification
10:42	PARCEL: Feature subset selection
11:09	Gene Expression Microarrays
14:59	Inference problems in Microarray Data
16:33	Gene Expression Microarrays
16:38	Inference problems in Microarray Data
18:21	Subspaces of gene expressions
19:07	Yeast Gene Classification: [ Switch to MATLAB here ]
22:20	Discriminant Subspaces
23:01	Seemingly similar models
24:13	Algorithm
25:20	Another view…
26:05	Another view…
27:04	Block diagonal scatter matrix
27:29	Simulations
27:37	Simulations
28:29	AML / ALL Leukaemia data
29:46	Conclusions

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