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@PHDTHESIS{valko2011adaptive,
  author = {Michal Valko},
  title = {Adaptive Graph-Based Algorithms for Conditional Anomaly Detection
	and Semi-Supervised Learning},
  school = {University of Pittsburgh},
  year = {2011},
  month = {August},
  abstract = {We develop graph-based methods for semi-supervised learning based
	on label propagation on a data similarity graph. When data is abundant
	or arrive in a stream, the problems of computation and data storage
	arise for any graph-based method. We propose a fast approximate online
	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 also present graph-based methods for detecting conditional anomalies
	and apply them to the identification of unusual clinical actions
	in hospitals.  Our hypothesis is that patient-management actions
	that are unusual with respect to the past patients may be due to
	errors and that it is worthwhile to raise an alert if such a condition
	is encountered. Conditional anomaly detection extends standard unconditional
	anomaly framework but also faces new problems known as fringe and
	isolated points. We devise novel nonparametric graph-based methods
	to tackle these problems. Our methods rely on graph connectivity
	analysis and soft harmonic solution. Finally, we conduct an extensive
	human evaluation study of our conditional anomaly methods by 15 experts
	in critical care.},
  owner = {misovalko},
  timestamp = {2011.08.18}
}