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This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. raining principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning, causing a proliferation of semisupervised training methods. In this paper we present a simple unification of several supervised and unsupervised training principles through the concept of optimal reverse prediction: predict the inputs from the target labels, optimizing both over model parameters and any missing labels. In particular, we show how supervised least squares, principal components analysis, k-means clustering and normalized graph-cut clustering can all be expressed as instances of the same training principle, differing only in constraints made on the target labels. Natural forms of semi-supervised regression and classification are then automatically derived, yielding semi-supervised learning algorithms for regression and classification that, surprisingly, are novel and refine the state of the art. These algorithms can all be combined with standard regularizers and made non-linear via kernels.

Disciplines:

• Science and Technology  / Computer Science  / Programming & Programming Languages