Canonical polyadic decomposition for unsupervised linear feature extraction from protein profiles

A. Jukic, I. Kopriva, A. Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

We propose a method for unsupervised linear feature extraction through tensor decomposition. The linear feature extraction can be formulated as a canonical polyadic decomposition (CPD) of a third-order tensor when transformation matrix is constrained to be equal to the Khatri-Rao product of two matrices. Therefore, standard algorithms for computing CPD decomposition can be used for feature extraction. The proposed method is validated on publicly available low-resolutionmass spectra of cancerous and non-cancerous samples. Obtained results indicate that this approach could be of practical importance in analysis of protein expression profiles.

Original languageEnglish
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
Publication statusPublished - 2013
Externally publishedYes
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: 9 Sep 201313 Sep 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference2013 21st European Signal Processing Conference, EUSIPCO 2013
Country/TerritoryMorocco
CityMarrakech
Period9/09/1313/09/13

Keywords

  • cancer prediction
  • Feature extraction
  • tensor decomposition

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