Iterative Supervised Principal Components

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Details

Original languageEnglish
Title of host publicationInternational Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands
EditorsAmos Storkey, Fernando Perez-Cruz
Number of pages9
StatePublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Lanzarote, Spain
Duration: 9 Apr 201821 Apr 2018
Conference number: 21

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume84
ISSN (Electronic)1938-7228

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS
CountrySpain
CityLanzarote
Period09/04/201821/04/2018

Researchers

Research units

Abstract

In high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known to produce good results but is computationally challenging. To alleviate this computational burden, we propose to use a preprocessing step where we first apply a dimension reduction to the original data to reduce the number of features to something that is computationally conveniently handled by Bayesian methods. To do this, we propose a new dimension reduction technique, called iterative supervised principal components (ISPCs), which combines variable screening and dimension reduction and can be considered as an extension to the existing technique of supervised principal components (SPCs). Our empirical evaluations confirm that, although not foolproof, the proposed approach provides very good results on several microarray benchmark datasets with very affordable computation time, and can also be very useful for visualizing high-dimensional data.

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