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» Supervised dimensionality reduction using mixture models
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NIPS
2003
15 years 1 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
IRI
2007
IEEE
15 years 6 months ago
Enhancing Text Analysis via Dimensionality Reduction
Many applications require analyzing vast amounts of textual data, but the size and inherent noise of such data can make processing very challenging. One approach to these issues i...
David G. Underhill, Luke McDowell, David J. Marche...
PAMI
2007
154views more  PAMI 2007»
14 years 11 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...
CIVR
2008
Springer
271views Image Analysis» more  CIVR 2008»
15 years 1 months ago
Multiple feature fusion by subspace learning
Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion t...
Yun Fu, Liangliang Cao, Guodong Guo, Thomas S. Hua...
CORR
2011
Springer
151views Education» more  CORR 2011»
14 years 6 months ago
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject’s behavior during a scanning se...
Vincent Michel, Alexandre Gramfort, Gaël Varo...