Active learning on manifolds
Active learning on manifolds
复制标题
流形上的主动学习
DOI：
10.1016/j.neucom.2013.08.002
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发表时间：
2014
期刊：
影响因子：
6
通讯作者：
Deng Cai
中科院分区：
文献类型：
--
作者：
Haifeng Liu;Haifeng Liu;Deng Cai;Deng Cai
Due to the rapid growth of the size of the digital information available, it is often impossible to label all the samples. Thus, it is crucial to select the most informative samples to label so that the learning performance can be most improved with limited labels. Many active learning algorithms have been proposed for this purpose. Most of these approaches effectively discover the Euclidean structure of the data space, whereas the geometrical (manifold) structure is not well respected. In this paper, we propose a novel active learning algorithm which explicitly considers the case that the data are sampled from a low dimensional sub-manifold embedded in the high dimensional ambient space. The geodesic distance of two data points on the manifold is estimated by the shortest-path distance between the two corresponding vertices in the nearest neighbor graph. By selecting the most representative points with respect to the manifold structure, our approach can effectively decrease the number of training examples the learner needs in order to achieve good performance. Experimental results on visual objects recognition and text categorization have demonstrated the effectiveness of our proposed approach.
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DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
M. Belkin;P. Niyogi;Vikas Sindhwani
通讯作者：
M. Belkin;P. Niyogi;Vikas Sindhwani
影响因子：
2.5
作者：
E. Ziegel
通讯作者：
E. Ziegel
DOI：
--
发表时间：
2008-12
期刊：
--
影响因子：
--
作者：
Sudheendra Vijayanarasimhan;K. Grauman
通讯作者：
Sudheendra Vijayanarasimhan;K. Grauman
影响因子：
0.7
作者：
W. Näther
通讯作者：
W. Näther
DOI：
10.1109/iccv.2007.4408844
发表时间：
2007-12
期刊：
2007 IEEE 11th International Conference on Computer Vision
影响因子：
--
作者：
Ashish Kapoor;K. Grauman;R. Urtasun;Trevor Darrell
通讯作者：
Ashish Kapoor;K. Grauman;R. Urtasun;Trevor Darrell