SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding

	SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding
	Courty, Nicolas 1; Gong, Xing 2,3; Vandel, Jimmy 4; Burger, Thomas 4
发表期刊	MACHINE LEARNING
	2014-10-01
卷号	97 期号:1-2 页码:205-226
文章类型	Article
摘要	This paper presents a new non-negative matrix factorization technique which (1) allows the decomposition of the original data on multiple latent factors accounting for the geometrical structure of the manifold embedding the data; (2) provides an optimal representation with a controllable level of sparsity; (3) has an overall linear complexity allowing handling in tractable time large and high dimensional datasets. It operates by coding the data with respect to local neighbors with non-linear weights. This locality is obtained as a consequence of the simultaneous sparsity and convexity constraints. Our method is demonstrated over several experiments, including a feature extraction and classification task, where it achieves better performances than the state-of-the-art factorization methods, with a shorter computational time.
关键词	Non-negative Matrix Factorization Manifold Sampling Kernel Methods Sparse Projections Simplex Methods Convexity Constraints
WOS标题词	Science & Technology ; Technology
关键词[WOS]	DIMENSIONALITY REDUCTION ; ARCHETYPAL ANALYSIS ; KERNELS ; SPACE
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000341431300010
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8027
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Univ Bretagne Sud, IRISA, Vannes, France 2.Univ Rennes 2, Costel, F-35043 Rennes, France 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 4.Univ Grenoble Alpes, CNRS FR3425, INSERM U1038, CEA iRSTV BGE, Grenoble, France
推荐引用方式 GB/T 7714	Courty, Nicolas,Gong, Xing,Vandel, Jimmy,et al. SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding[J]. MACHINE LEARNING,2014,97(1-2):205-226.
APA	Courty, Nicolas,Gong, Xing,Vandel, Jimmy,&Burger, Thomas.(2014).SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding.MACHINE LEARNING,97(1-2),205-226.
MLA	Courty, Nicolas,et al."SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding".MACHINE LEARNING 97.1-2(2014):205-226.