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» Clustering functional data with the SOM algorithm
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167
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CVPR
2009
IEEE
16 years 10 months ago
Unsupervised Maximum Margin Feature Selection with Manifold Regularization
Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature s...
Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui ...
144
Voted
ICDM
2010
IEEE
226views Data Mining» more  ICDM 2010»
15 years 1 months ago
Edge Weight Regularization over Multiple Graphs for Similarity Learning
The growth of the web has directly influenced the increase in the availability of relational data. One of the key problems in mining such data is computing the similarity between o...
Pradeep Muthukrishnan, Dragomir R. Radev, Qiaozhu ...
124
Voted
ISMB
2000
15 years 5 months ago
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
The immensevolumeof data resulting from DNAmicroarray experiments, accompaniedby an increase in the numberof publications discussing gene-related discoveries, presents a majordata...
Hagit Shatkay, Stephen Edwards, W. John Wilbur, Ma...
113
Voted
PG
2007
IEEE
15 years 10 months ago
Precomputed Visibility Cuts for Interactive Relighting with Dynamic BRDFs
This paper presents a novel PRT-based method that uses precomputed visibility cuts for interactive relighting with all-frequency environment maps and arbitrary dynamic BRDFs. Our ...
Oskar Åkerlund, Mattias Unger, Rui Wang
158
Voted
ISBI
2009
IEEE
15 years 10 months ago
Image-Driven Population Analysis Through Mixture Modeling
—We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of...
Mert R. Sabuncu