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TNN
2008
128views more  TNN 2008»
13 years 7 months ago
Nonnegative Matrix Factorization in Polynomial Feature Space
Abstract--Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), indepe...
Ioan Buciu, Nikos Nikolaidis, Ioannis Pitas
ICDM
2009
IEEE
202views Data Mining» more  ICDM 2009»
13 years 5 months ago
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
Abstract--The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In t...
Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda
COST
2010
Springer
131views Multimedia» more  COST 2010»
13 years 2 months ago
Understanding Parent-Infant Behaviors Using Non-negative Matrix Factorization
Abstract. There are considerable differences among infants in the quality of interaction with their parents. These differences depend especially on the infants development which af...
Ammar Mahdhaoui, Mohamed Chetouani
ACCV
2006
Springer
13 years 11 months ago
Multiple Similarities Based Kernel Subspace Learning for Image Classification
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
COMBINATORICS
2006
221views more  COMBINATORICS 2006»
13 years 7 months ago
Kernels of Directed Graph Laplacians
Abstract. Let G denote a directed graph with adjacency matrix Q and indegree matrix D. We consider the Kirchhoff matrix L = D - Q, sometimes referred to as the directed Laplacian. ...
John S. Caughman IV, J. J. P. Veerman