Novel Unsupervised Feature Filtering of Biological Data | Zendy

Roy Varshavsky | Zendy; Assaf Gottlieb | Zendy; Michal Linial | Zendy; D. Horn | Zendy

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Novel Unsupervised Feature Filtering of Biological Data

Author(s) -

Roy Varshavsky,

Assaf Gottlieb,

Michal Linial,

D. Horn

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl214

Subject(s) - pattern recognition (psychology) , feature selection , computer science , jaccard index , cluster analysis , entropy (arrow of time) , feature (linguistics) , principal component analysis , artificial intelligence , data mining , projection (relational algebra) , algorithm , linguistics , philosophy , physics , quantum mechanics

Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR); forward selection by accumulating features according to which set produces highest entropy (FS1); forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2); backward elimination (BE) of features with the lowest CE.

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