Please use this identifier to cite or link to this item:
|dc.title||Hyperparameter learning for graph based semi-supervised learning algorithms|
|dc.identifier.citation||Zhang, 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.description.abstract||Semi-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.description.sourcetitle||Advances in Neural Information Processing Systems|
|Appears in Collections:||Staff Publications|
Show simple item record
Files in This Item:
There are no files associated with this item.
checked on Mar 31, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.