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FTML
2010
159views more  FTML 2010»
13 years 7 months ago
Dimension Reduction: A Guided Tour
We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the da...
Christopher J. C. Burges
ER
2004
Springer
103views Database» more  ER 2004»
14 years 1 months ago
Modeling Default Induction with Conceptual Structures
Our goal is to model the way people induce knowledge from rare and sparse data. This paper describes a theoretical framework for inducing knowledge from these incomplete data descr...
Julien Velcin, Jean-Gabriel Ganascia
IPL
2008
78views more  IPL 2008»
13 years 8 months ago
An approximation ratio for biclustering
The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters ...
Kai Puolamäki, Sami Hanhijärvi, Gemma C....
CIKM
2009
Springer
14 years 3 months ago
Scalable learning of collective behavior based on sparse social dimensions
The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr a...
Lei Tang, Huan Liu
VISSYM
2003
13 years 9 months ago
Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets
Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimension...
Jing Yang, Matthew O. Ward, Elke A. Rundensteiner,...