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	Locating High-density Clusters with Noisy Queries
	Chen Cao; Shifeng Chen; Changqing Zou
	2012
会议名称	21st International Conference on Pattern Recognition (ICPR)
会议地点	日本
英文摘要	Semi-supervised learning (SSL) relies on a few labeled samples to explore data’s intrinsic structure through pairwise smooth transduction. The performance of SSL mainly depends on two folds: (1) the accuracy of labeled queries, (2) the integrity of manifolds in data distribution. Both of these qualities would be poor in real applications as data often consist of several irrelevant clusters and discrete noise. In this paper we propose a novel framework to simultaneously remove discrete noise and locate the high-density clusters. Experiments demonstrate that our algorithm is quite effective to solve several problems such as non-feedback image re-ranking and image co-segmentation.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3792]
专题	深圳先进技术研究院_集成所
作者单位	2012
推荐引用方式 GB/T 7714	Chen Cao,Shifeng Chen,Changqing Zou. Locating High-density Clusters with Noisy Queries[C]. 见:21st International Conference on Pattern Recognition (ICPR). 日本.

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