Heterogeneous component analysis

Shigeyuki Oba, Motoaki Kawanabe, Klaus Muller, Shin Ishii

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

1 Citation (Scopus)

Abstract

In bioinformatics it is often desirable to combine data from various measurement sources and thus structured feature vectors are to be analyzed that possess different intrinsic blocking characteristics (e.g., different patterns of missing values, observation noise levels, effective intrinsic dimensionalities). We propose a new machine learning tool, heterogeneous component analysis (HCA), for feature extraction in order to better understand the factors that underlie such complex structured heterogeneous data. HCA is a linear block-wise sparse Bayesian PCA based not only on a probabilistic model with block-wise residual variance terms but also on a Bayesian treatment of a block-wise sparse factor-loading matrix. We study various algorithms that implement our HCA concept extracting sparse heterogeneous structure by obtaining common components for the blocks and specific components within each block. Simulations on toy and bioinformatics data underline the usefulness of the proposed structured matrix factorization concept.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada
Duration: 2007 Dec 32007 Dec 6

Other

Other21st Annual Conference on Neural Information Processing Systems, NIPS 2007
CountryCanada
CityVancouver, BC
Period07/12/307/12/6

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ASJC Scopus subject areas

  • Information Systems

Cite this

Oba, S., Kawanabe, M., Muller, K., & Ishii, S. (2009). Heterogeneous component analysis. In Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference