Order-preserving factor discovery from misaligned data

Arnau Tibau Puig, Ami Wiesel, Aimee Zaas, Geoffrey S. Ginsburg, Gilles Fleury, Alfred O. Hero

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

1 Scopus citations

Abstract

We present a factor analysis method that accounts for possible temporal misalignment of the factor loadings across the population of samples. Our main hypothesis is that the data contains a subset of variables with similar but delayed profiles obeying a consistent precedence ordering relationship. Our model is motivated by the difficulty of gene expression analysis across subjects who have common patterns of immune response but show different onset times after a uniform innoculation time of a viral pathogen. The proposed method is based on a linear model with additional degrees of freedom that account for each subject's inherent delays. We present an algorithm to fit this model in a totally unsupervised manner and demonstrate its effectiveness on extracting gene expression factors affecting host response using a flu-virus human challenge study dataset.

Original languageAmerican English
Title of host publication2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Pages209-212
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010 - Jerusalem, Israel
Duration: 4 Oct 20107 Oct 2010

Publication series

Name2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010

Conference

Conference2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Country/TerritoryIsrael
CityJerusalem
Period4/10/107/10/10

Keywords

  • Dictionary learning
  • Low-rank matrix approximation
  • Parallel factor analysis

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