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» Closed-form supervised dimensionality reduction with general...
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CVPR
2005
IEEE
14 years 7 months 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...
PAMI
2007
154views more  PAMI 2007»
13 years 5 months ago
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
—Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different soluti...
Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zha...
NIPS
2008
13 years 6 months ago
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
Simon Lacoste-Julien, Fei Sha, Michael I. Jordan
NIPS
2003
13 years 6 months ago
Non-linear CCA and PCA by Alignment of Local Models
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of...
Jakob J. Verbeek, Sam T. Roweis, Nikos A. Vlassis
NIPS
2003
13 years 6 months ago
Linear Dependent Dimensionality Reduction
We formulate linear dimensionality reduction as a semi-parametric estimation problem, enabling us to study its asymptotic behavior. We generalize the problem beyond additive Gauss...
Nathan Srebro, Tommi Jaakkola