CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding
Courty, Nicolas1; Gong, Xing2,3; Vandel, Jimmy4; Burger, Thomas4
Source PublicationMACHINE LEARNING
2014-10-01
Volume97Issue:1-2Pages:205-226
SubtypeArticle
AbstractThis 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.
KeywordNon-negative Matrix Factorization Manifold Sampling Kernel Methods Sparse Projections Simplex Methods Convexity Constraints
WOS HeadingsScience & Technology ; Technology
WOS KeywordDIMENSIONALITY REDUCTION ; ARCHETYPAL ANALYSIS ; KERNELS ; SPACE
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000341431300010
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8027
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.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
Recommended Citation
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.
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