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» Kernels of Directed Graph Laplacians
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ICML
2007
IEEE
14 years 6 months ago
Learning random walks to rank nodes in graphs
Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalizat...
Alekh Agarwal, Soumen Chakrabarti
ICPR
2008
IEEE
13 years 11 months ago
Object recognition using graph spectral invariants
Graph structures have been proved important in high level-vision since they can be used to represent structural and relational arrangements of objects in a scene. One of the probl...
Bai Xiao, Richard C. Wilson, Edwin R. Hancock
ICML
2007
IEEE
14 years 6 months ago
Regression on manifolds using kernel dimension reduction
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
Jens Nilsson, Fei Sha, Michael I. Jordan
ICML
2007
IEEE
14 years 6 months ago
Learning nonparametric kernel matrices from pairwise constraints
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Som...
Steven C. H. Hoi, Rong Jin, Michael R. Lyu
ECAI
2008
Springer
13 years 7 months ago
Modeling Collaborative Similarity with the Signed Resistance Distance Kernel
We extend the resistance distance kernel to the domain of signed dissimilarity values, and show how it can be applied to collaborative rating prediction. The resistance distance is...
Jérôme Kunegis, Stephan Schmidt, Sahi...