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2003 JMLR JMLR 2003

The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Abstract

In biological data, it is often the case that observed data are available only for a subset of samples. When a kernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. In this paper, the missing entries are completed by exploiting an auxiliary kernel matrix derived from another information source. The parametric model of kernel matrices is created as a set of spectral variants of the auxiliary kernel matrix, and the missing entries are estimated by fitting this model to the existing entries. For model fitting, we adopt the em algorithm (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 bacteria clustering experiments using two marker sequences: 16S and gyrB. [abs] [ pdf ][ ps.gz ][ ps ]

📈 Trend Setter — Clustering
🧭 Keyword Pioneer — em algorithm
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Koji Tsuda , Shotaro Akaho , Kiyoshi Asai

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Optimization & Theory > Optimization

Keywords

em algorithm auxiliary datum positive definite matrix kernel matrix completion bacteria clustering
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