Neighborhood co-regularized multi-view spectral clustering of microbiome data
conference paper
In many unsupervised learning problems data can be available in different representations, often referred to as views. By leveraging information from multiple views we can obtain clustering that is more robust and accurate compared to the one obtained via the individual views. We propose a novel algorithm that is based on neighborhood co-regularization of the clustering hypotheses and that searches for the solution which is consistent across different views. In our empirical evaluation on publicly available datasets, the proposed method outperforms several state-of-the-art clustering algorithms. Furthermore, application of our method to recently collected biomedical data leads to new insights, critical for future research on determinants of the cervicovaginal microbiome and the cervicovaginal microbiome as a risk factor for the transmission of HIV. These insights could have an influence on the interpretation of clinical presentation of women with bacterial vaginosis and treatment decisions. © 2013 Springer-Verlag.
Topics
TNO Identifier
485675
ISSN
03029743
ISBN
9783642407048
Source title
2nd IAPR International Workshop on Partially Supervised Learning, PSL 2013, 13 May 2013 through 14 May 2013, Nanjing
Pages
80-90
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