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» Dimensionality Reduction for Classification
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WIRN
2005
Springer
15 years 8 months ago
Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
Alberto Bertoni, Giorgio Valentini
CVPR
2008
IEEE
16 years 5 months ago
Scene classification with low-dimensional semantic spaces and weak supervision
A novel approach to scene categorization is proposed. Similar to previous works of [11, 15, 3, 12], we introduce an intermediate space, based on a low dimensional semantic "t...
Nikhil Rasiwasia, Nuno Vasconcelos
ICDM
2008
IEEE
164views Data Mining» more  ICDM 2008»
15 years 9 months ago
Classifying High-Dimensional Text and Web Data Using Very Short Patterns
In this paper, we propose the "Democratic Classifier", a simple, democracy-inspired patternbased classification algorithm that uses very short patterns for classificatio...
Hassan H. Malik, John R. Kender
CDC
2008
IEEE
118views Control Systems» more  CDC 2008»
15 years 9 months ago
A density projection approach to dimension reduction for continuous-state POMDPs
Abstract— Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms ...
Enlu Zhou, Michael C. Fu, Steven I. Marcus
PR
2006
229views more  PR 2006»
15 years 3 months ago
FS_SFS: A novel feature selection method for support vector machines
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...
Yi Liu, Yuan F. Zheng