Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/41546
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dc.titleHyperparameter learning for graph based semi-supervised learning algorithms
dc.contributor.authorZhang, X.
dc.contributor.authorLee, W.S.
dc.date.accessioned2013-07-04T08:30:04Z
dc.date.available2013-07-04T08:30:04Z
dc.date.issued2007
dc.identifier.citationZhang, X.,Lee, W.S. (2007). Hyperparameter learning for graph based semi-supervised learning algorithms. Advances in Neural Information Processing Systems : 1585-1592. ScholarBank@NUS Repository.
dc.identifier.isbn9780262195683
dc.identifier.issn10495258
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41546
dc.description.abstractSemi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method; this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleAdvances in Neural Information Processing Systems
dc.description.page1585-1592
dc.identifier.isiutNOT_IN_WOS
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