Safe Semi-Supervised Learning of Sum-Product Networks

TitleSafe Semi-Supervised Learning of Sum-Product Networks
Publication TypeConference Proceedings
Year of Publication2017
AuthorsTrapp, M., Madl T., Peharz R., Pernkopf F., & Trappl R.
Conference NameConference on Uncertainty in Artificial Intelligence (UAI)
Abstract

While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such approaches have only been proposed for linear models. In this work, we introduce semi-supervised parameter learning for Sum-Product Networks (SPNs). SPNs are deep probabilistic models admitting inference in linear time in number of network edges. Our approach has several advantages, as it (1) allows generative and discriminative semi-supervised learning, (2) guarantees that adding unlabelled data can increase, but not degrade, the performance (safe), and (3) is computationally efficient and does not enforce restrictive assumptions on the data distribution. We show on a variety of data sets that safe semi-supervised learning with SPNs is competitive compared to state-of-the-art and can lead to a better generative and discriminative objective value than a purely supervised approach.

Citation Key3611
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