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@INPROCEEDINGS{valko2010online,
  author = {Michal Valko and Branislav Kveton and Ling Huang and Daniel Ting},
  title = {Online Semi-Supervised Learning on Quantized Graphs},
  booktitle = {Proceedings of the 26nd Annual Conference on Uncertainty in Artificial
	Intelligence},
  year = {2010},
  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.},
  owner = {misovalko},
  timestamp = {2010.06.06}
}