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CVPR
2006
IEEE
11 years 20 days 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
CVPR
2005
IEEE
11 years 20 days ago
Multi-Output Regularized Projection
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based o...
Kai Yu, Shipeng Yu, Volker Tresp
CVPR
2005
IEEE
11 years 20 days ago
Graph Embedding: A General Framework for Dimensionality Reduction
In the last decades, a large family of algorithms supervised or unsupervised; stemming from statistic or geometry theory have been proposed to provide different solutions to the p...
Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zha...
ICCV
2009
IEEE
11 years 3 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker

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prsinghalStudent
Indiana University
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