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	Sparse manifold learning and its applications in image classification
	Li, Wangmu ; Yu, Jun ; Yu J(俞俊)
	2014
关键词	Algorithms Experiments Face recognition Internet Motion compensation
英文摘要	Conference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.; National Natural Foundation of China; SIGMM China Chapter; Xiamen University; Graph-based dimensionality reduction algorithms are important and have been commonly applied in image classification and computer vision applications. To date many approaches have been proposed, e.g. Laplacian Eigenmaps (LE), Locally Linear Embedding (LLE), Locality Preserving Projections (LPP) and ISOMAP and so on. However, all these methods need to set the k nearest neighbor parameter to address the problem. In this paper, we proposed Sparse Patch Alignment Framework to settle it. Patch Alignment Framework which unified manifold learning algorithms through two stages: local patch optimization and whole alignment. We use Sparse Coding to construct the local patch instead of using KNN, thus, the k nearest neighbor parameter is set adaptively. A lot of experiments are done to show the performance of our method. The experiment results illustrate that our method is stable and robust. Copyright 2014 ACM.
语种	英语
出处	http://dx.doi.org/10.1145/2632856.2632886
出版者	Association for Computing Machinery
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86869]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Li, Wangmu,Yu, Jun,Yu J. Sparse manifold learning and its applications in image classification. 2014-01-01.

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