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Article overview
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Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization | | Alexander Jung
; Alfred O. Hero III
; Alexandru Mara
; Sabeur Aridhi
; | | Date: |
2 Nov 2016 | | Abstract: | We propose a scalable method for semi-supervised (transductive) learning from
massive network-structured datasets. Our approach to semi-supervised learning
is based on representing the underlying hypothesis as a graph signal with small
total variation. Requiring a small total variation of the graph signal
representing the underlying hypothesis corresponds to the central smoothness
assumption that forms the basis for semi-supervised learning, i.e., input
points forming clusters have similar output values or labels. We formulate the
learning problem as a nonsmooth convex optimization problem which we solve by
appealing to Nesterovs optimal first-order method for nonsmooth optimization.
We also provide a message passing formulation of the learning method which
allows for a highly scalable implementation in big data frameworks. | | Source: | arXiv, 1611.0714 | | Services: | Forum | Review | PDF | Favorites | |
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