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ICPR
2010
IEEE
13 years 10 months ago
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...
Michal Lewandowski, Jesus Martinez-Del-Rincon, Dim...
PAMI
2011
13 years 2 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ICDE
2002
IEEE
91views Database» more  ICDE 2002»
14 years 22 days ago
Lossy Reduction for Very High Dimensional Data
We consider the use of data reduction techniques for the problem of approximate query answering. We focus on applications for which accurate answers to selective queries are requi...
Chris Jermaine, Edward Omiecinski
IDEAL
2004
Springer
14 years 1 months ago
Dimensionality Reduction with Image Data
A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We pro...
Mónica Benito, Daniel Peña
NIPS
2003
13 years 9 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