Seminar: Principled Non-Linear Feature Selection (with applications in representation learning)

SpeakerDimitrios Athanasakis
AffiliationUCL
DateFriday, 11 Apr 2014
Time13:00 - 14:00
LocationMalet Place Engineering 1.03
Event seriesMicrosoft Research CSML Seminar Series
Description

Following recent work in non-linear feature selection we propose a novel method for assessing the contribution of
a feature through estimating its expected impact on the alignment or HSIC measure. Theoretical analysis of this
approach is included showing that for appropriate polynomial sample sizes influential features can be distinguished
from irrelevant ones. We present experimental evidence which confirm the analysis including applications in representation learning.
The method was used to obtain a 3rd position result in the 2013 ICML black box learning challenge, as well as competitive results in
signal peptide prediction, an important bioinformatics application.

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