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CVPR
2005
IEEE
16 years 2 months ago
Beyond Pairwise Clustering
We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the ...
Sameer Agarwal, Jongwoo Lim, Lihi Zelnik-Manor, Pi...
91
Voted
SDM
2009
SIAM
114views Data Mining» more  SDM 2009»
15 years 9 months ago
Top-k Correlative Graph Mining.
Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases ...
Yiping Ke, James Cheng, Jeffrey Xu Yu
101
Voted
IJCNN
2006
IEEE
15 years 6 months ago
Sparse Bayesian Models: Bankruptcy-Predictors of Choice?
Abstract— Making inferences and choosing appropriate responses based on incomplete, uncertainty and noisy data is challenging in financial settings particularly in bankruptcy de...
Bernardete Ribeiro, Armando Vieira, João Ca...
98
Voted
JSAC
2008
85views more  JSAC 2008»
15 years 17 days ago
A distributed minimum variance estimator for sensor networks
A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its es...
Alberto Speranzon, Carlo Fischione, Karl Henrik Jo...
118
Voted
LATIN
2010
Springer
15 years 7 months ago
Sharp Separation and Applications to Exact and Parameterized Algorithms
Many divide-and-conquer algorithms employ the fact that the vertex set of a graph of bounded treewidth can be separated in two roughly balanced subsets by removing a small subset o...
Fedor V. Fomin, Daniel Lokshtanov, Fabrizio Grando...