Uncertainty in Artificial Intelligence
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Online Semi-Supervised Learning on Quantized Graphs
Michal Valko, Branislav Kveton, Ling Huang, Daniel Ting
Abstract:
In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.
Keywords:
Pages: 606-614
PS Link:
PDF Link: /papers/10/p606-valko.pdf
BibTex:
@INPROCEEDINGS{Valko10,
AUTHOR = "Michal Valko and Branislav Kveton and Ling Huang and Daniel Ting",
TITLE = "Online Semi-Supervised Learning on Quantized Graphs",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "606--614"
}


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