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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen Cao Shifeng Chen Changqing Zou Jianzhuang Liu |
| Copyright Year | 2012 |
| Description | Author affiliation: Shenzhen Key Laboratory for Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China (Chen Cao; Shifeng Chen; Changqing Zou; Jianzhuang Liu) |
| Abstract | 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. |
| Starting Page | 3537 |
| Ending Page | 3540 |
| File Size | 260862 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781467322164 |
| ISSN | 10514651 |
| e-ISBN | 9784990644109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-11 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | ICPR Org Committee |
| Subject Keyword | Noise Noise measurement Clustering algorithms Databases Vectors Manifolds Semisupervised learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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