Abstract
Semi-supervised learning is an eminent domain of machine learning focusing on real-life problems where the labeled data instances are scarce. This paper innovatively extends existing factorization models into a supervised nonlinear factorization. The current state of the art methods for semi-supervised regression are based on supervised manifold regularization. In contrast, the latent data constructed by the proposed method jointly reconstructs both the observed predictors and target variables via generative-style nonlinear functions. Dual-form solutions of the nonlinear functions and a stochastic gradient descent technique which learns the low dimensionality data are introduced. The validity of our method is demonstrated in a series of experiments against five state-of-art baselines, clearly improving the prediction accuracy in eleven real-life data sets.
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Grabocka, J., Bedalli, E., Schmidt-Thieme, L. (2014). Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_16
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DOI: https://doi.org/10.1007/978-3-319-06608-0_16
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