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	Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
	Li, Chun-Guang ; Lin, Zhouchen ; Zhang, Honggang ; Guo, Jun
	2015
关键词	LABEL PROPAGATION RECOGNITION REDUCTION
英文摘要	State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework - constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels. While such a two-stage framework has been successful in many applications, solving two subproblems separately only once is still suboptimal because it does not fully exploit the correlation between the affinity and the labels. In this paper, we formulate the two stages of SSL into a unified optimization framework, which learns both the affinity matrix and the unknown labels simultaneously. In the unified framework, both the given labels and the estimated labels are used to learn the affinity matrix and to infer the unknown labels. We solve the unified optimization problem via an alternating direction method of multipliers combined with label propagation. Extensive experiments on a synthetic data set and several benchmark data sets demonstrate the effectiveness of our approach.; EI; CPCI-S(ISTP); lichunguang@bupt.edu.cn; zlin@pku.edu.cn; zhhg@bupt.edu.cn; guojun@bupt.edu.cn; 2767-2775; 11-18-December-2015
语种	英语
出处	IEEE International Conference on Computer Vision
DOI标识	10.1109/ICCV.2015.317
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436290]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Chun-Guang,Lin, Zhouchen,Zhang, Honggang,et al. Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning. 2015-01-01.

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