Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to bes...Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.展开更多
Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affe...Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods.展开更多
The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear ...The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation.展开更多
基金supported by the DOD National Defense Science and Engineering Graduate(NDSEG)Research Fellowshipsupported by the NGA under Contract No.HM04762110003.
文摘Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.
文摘Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.60835002,61075004).
文摘The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation.
