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» Multi-Instance Dimensionality Reduction
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ICML
2005
IEEE
14 years 8 months ago
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
Fei Sha, Lawrence K. Saul
ACMACE
2007
ACM
13 years 11 months ago
Application of dimensionality reduction techniques to HRTFS for interactive virtual environments
Fundamental to the generation of 3D audio is the HRTF processing of acoustical signals. Unfortunately, given the high dimensionality of HRTFs, incorporating them into dynamic/inte...
Bill Kapralos, Nathan Mekuz
CVPR
2006
IEEE
14 years 9 months ago
Dimensionality Reduction by Learning an Invariant Mapping
Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that "similar" points in input space are mapped to ne...
Raia Hadsell, Sumit Chopra, Yann LeCun
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,...
CVPR
2008
IEEE
14 years 9 months ago
Semi-Supervised Discriminant Analysis using robust path-based similarity
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
Yu Zhang, Dit-Yan Yeung