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APVIS
2010
13 years 8 months ago
GMap: Visualizing graphs and clusters as maps
Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through...
Emden R. Gansner, Yifan Hu, Stephen G. Kobourov
HAIS
2010
Springer
13 years 5 months ago
Reducing Dimensionality in Multiple Instance Learning with a Filter Method
In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning. Multiple instance learning is considered a...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
SAC
2005
ACM
14 years 29 days ago
Estimating manifold dimension by inversion error
Video and image datasets can often be described by a small number of parameters, even though each image usually consists of hundreds or thousands of pixels. This observation is of...
Shawn Martin, Alex Bäcker
UM
2009
Springer
14 years 1 months ago
PerspectiveSpace: Opinion Modeling with Dimensionality Reduction
Abstract. Words mean different things to different people, and capturing these differences is often a subtle art. These differences are often “a matter of perspective,” and...
Jason B. Alonso, Catherine Havasi, Henry Lieberman
KDD
2003
ACM
127views Data Mining» more  KDD 2003»
14 years 7 months ago
Experiments with random projections for machine learning
Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computation...
Dmitriy Fradkin, David Madigan