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Heterogeneous Component Analysis
Shigeyuki Oba · Motoaki Kawanabe · Klaus-Robert Müller · Shin Ishii

Tue Dec 04 09:50 AM -- 10:00 AM (PST) @ None

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 specic components within each block. Simulations on toy and bioinformatics data underline the usefulness of the proposed structured matrix factorization concept.

Author Information

Shigeyuki Oba (Kyoto University)
Motoaki Kawanabe (Fraunhofer FIRST)
Klaus-Robert Müller (TU Berlin)
Shin Ishii (Kyoto University)

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