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What is the Optimal Number of Features? A learning theoretic perspective
Published on Feb 25, 20076905 Views
In this paper we discuss the problem of feature selection for supervised learning from the standpoint of statistical machine learning. We inquire what subset of features will lead to the best classifi
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Chapter list
What is The Optimal Number of Features? A learning theoretic Perspective 00:01
What is Feature Selection?00:18
Reasons to do Feature Selection01:09
The Questions02:10
Two Gaussians - Problem Setting02:42
Problem Setting – Cont.04:18
Illustration05:42
Result07:23
Solving for Specific 08:07
Solving for Specific - Cont.10:33
Problem Setting – Cont.11:40
Solving for Specific - Cont.11:59
Proof 12:13
Proof 14:24
Proof – Cont.14:49
Proof – Cont.15:53
“Empirical Proof” of the Lemma16:37
Linear SVM Error (averaged on 200 repeats, c=0.01, using Gavin Cawley’s tool box)17:25
Conclusions19:34
What is The Optimal Number of Features? A learning theoretic Perspective 21:09
Proof – Cont.24:30