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Self-Adjusting Models for Semi-supervised Learning in Partially Observed Settings

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4 Author(s)
Akova, F. ; Comput. & Inf. Sci. Dept., IUPUI, Indianapolis, IN, USA ; Dundar, M. ; Yuan Qi ; Rajwa, B.

We present a new direction for semi-supervised learning where self-adjusting generative models replace fixed ones and unlabeled data can potentially improve learning even when labeled data is only partially-observed. We model each class data by a mixture model and use a hierarchical Dirichlet process (HDP) to model observed as well as unobserved classes. We extend the standard HDP model to accommodate unlabeled samples and introduce a new sharing strategy, within the context of Gaussian mixture models, that restricts sharing with covariance matrices while leaving the mean vectors free. Our research is mainly driven by real-world applications with evolving data-generating mechanisms where obtaining a fully-observed labeled data set is impractical. We demonstrate the feasibility of the proposed approach for semi-supervised learning in two such applications.

Published in:

Data Mining (ICDM), 2012 IEEE 12th International Conference on

Date of Conference:

10-13 Dec. 2012