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

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	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
刊名	MACHINE LEARNING
	2014-10-01
卷号	97 期号:1-2 页码:205-226
关键词	Non-negative matrix factorization Manifold sampling Kernel methods Sparse projections Simplex methods Convexity constraints
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	DIMENSIONALITY REDUCTION ; ARCHETYPAL ANALYSIS ; KERNELS ; SPACE
收录类别	SCI
语种	英语
WOS记录号	WOS:000341431300010
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/8027]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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.