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» Lossy Reduction for Very High Dimensional Data
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CSDA
2008
108views more  CSDA 2008»
14 years 12 months ago
The random Tukey depth
The computation of the Tukey depth, also called halfspace depth, is very demanding, even in low dimensional spaces, because it requires that all possible one-dimensional projectio...
J. A. Cuesta-Albertos, A. Nieto-Reyes
CSB
2005
IEEE
125views Bioinformatics» more  CSB 2005»
15 years 5 months ago
On Optimizing Distance-Based Similarity Search for Biological Databases
Similarity search leveraging distance-based index structures is increasingly being used for both multimedia and biological database applications. We consider distance-based indexi...
Rui Mao, Weijia Xu, Smriti R. Ramakrishnan, Glen N...
FGR
2006
IEEE
170views Biometrics» more  FGR 2006»
15 years 5 months ago
The Isometric Self-Organizing Map for 3D Hand Pose Estimation
We propose an Isometric Self-Organizing Map (ISOSOM) method for nonlinear dimensionality reduction, which integrates a Self-Organizing Map model and an ISOMAP dimension reduction ...
Haiying Guan, Rogério Schmidt Feris, Matthe...
VISUALIZATION
2002
IEEE
15 years 4 months ago
Interactive Visualization of Complex Plant Ecosystems
We present a method for interactive rendering of large outdoor scenes. Complex polygonal plant models and whole plant populations are represented by relatively small sets of point...
Oliver Deussen, Carsten Colditz, Marc Stamminger, ...
ICML
2004
IEEE
16 years 18 days ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Ting Su, Jennifer G. Dy