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NIPS
2008
13 years 10 months ago
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ECCV
2006
Springer
14 years 10 months ago
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
Tony Lin, Hongbin Zha, Sang Uk Lee
ICPR
2006
IEEE
14 years 9 months ago
Motion Dependent Spatiotemporal Smoothing for Noise Reduction in Very Dim Light Image Sequences
A new method for noise reduction using spatiotemporal smoothing is presented in this paper. The method is developed especially for reducing the noise that arises when acquiring vi...
Henrik Malm, Eric Warrant
ICASSP
2008
IEEE
14 years 3 months ago
An ICA-based multilinear algebra tools for dimensionality reduction in hyperspectral imagery
Dimensionality reduction (DR) is a major issue to improve the efficiency of the classifiers in Hyperspectral images (HSI). Recently, the independent component analysis (ICA) app...
Nadine Renard, Salah Bourennane
ICASSP
2008
IEEE
14 years 3 months ago
Worst- and average-case complexity of LLL lattice reduction in MIMO wireless systems
Lattice reduction by means of the LLL algorithm has been previously suggested as a powerful preprocessing tool that allows to improve the performance of suboptimal detectors and t...
Joakim Jalden, Dominik Seethaler, Gerald Matz