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» Adaptive Sampling for Noisy Problems
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CVPR
2012
IEEE
11 years 10 months ago
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video
It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision prob...
Jun He, Laura Balzano, Arthur Szlam
ICPR
2008
IEEE
14 years 2 months ago
Incremental clustering via nonnegative matrix factorization
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF`s batch nature necessitates recomputation of whole basis set for new samples...
Serhat Selcuk Bucak, Bilge Günsel
CORR
2010
Springer
174views Education» more  CORR 2010»
13 years 7 months ago
Gaussian Process Bandits for Tree Search
We motivate and analyse a new Tree Search algorithm, based on recent advances in the use of Gaussian Processes for bandit problems. We assume that the function to maximise on the ...
Louis Dorard, John Shawe-Taylor
BMCBI
2010
125views more  BMCBI 2010»
13 years 7 months ago
In-silico prediction of blood-secretory human proteins using a ranking algorithm
Background: Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful inform...
Qi Liu, Juan Cui, Qiang Yang, Ying Xu
STACS
2007
Springer
14 years 1 months ago
Small Space Representations for Metric Min-Sum k -Clustering and Their Applications
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Artur Czumaj, Christian Sohler