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  The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., & Asai, K. (2003). The em Algorithm for Kernel Matrix Completion with Auxiliary Data. The Journal of Machine Learning Research, 4, 67-81.

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Tsuda, K1, 2, Author           
Akaho, S, Author
Asai, K, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In biological data, it is often the case that observed data are available only for a subset of samples. When akernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. Inthis paper, the missing entries are completed by exploiting an auxiliary kernel matrix derived from anotherinformation source. The parametric model of kernel matrices is created as a set of spectral variants of theauxiliary kernel matrix, and the missing entries are estimated by fitting this model to the existing entries. Formodel fitting, we adopt theemalgorithm (distinguished from the EM algorithm of Dempster et al., 1977)based on the information geometry of positive definite matrices. We will report promising results on bacteriaclustering experiments using two marker sequences: 16S and gyrB.

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 Dates: 2003-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2264
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 4 Sequence Number: - Start / End Page: 67 - 81 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1