Parallel Graph-Based Semi-Supervised Learning
Jeff Bilmes, Amarnag Subramanya · Cambridge University Press eBooks · 2011
Semi-supervised learning (SSL) is the process of training decision functions using small amounts of labeled and relatively large amounts of unlabeled data. In many applications, annotating training data is time consuming and error prone. Speech recognition is the typical example, which requires large amounts of meticulously annotated speech data (Evermann et al., 2005) to produce an accurate system. In the case of document classification for internet search, it is not even feasible to accurately annotate a relatively large number of web pages for all categories of potential interest. SSL lends itself as a useful technique in many machine learning applications because one need annotate only relatively small amounts of the available data. SSL is related to the problem of transductive learning (Vapnik, 1998). In general, a learner is transductive if it is designed for prediction on only a closed dataset, where the test set is revealed at training time. In practice, however, transductive learners can be modified to handle unseen data (Sindhwani, Niyogi, and Belkin, 2005; Zhu, 2005a). Chapter 25 in Chapelle, Scholkopf, and Zien (2007) gives a full discussion on the relationship between SSL and transductive learning. In this chapter, SSL refers to the semi-supervised transductive classification problem. Let x ∈ X denote the input to the decision function (classifier), f , and y ∈ Y denote its output label, that is, f : X → Y . In most cases f ( x ) = argmax y ∈Y p ( y | x ).