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ECCV
2004
Springer
15 years 11 months ago
Dimensionality Reduction by Canonical Contextual Correlation Projections
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
Marco Loog, Bram van Ginneken, Robert P. W. Duin
RSS
2007
152views Robotics» more  RSS 2007»
14 years 11 months ago
Dimensionality Reduction Using Automatic Supervision for Vision-Based Terrain Learning
Abstract— This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot’s mechanical sensors as ...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
ICASSP
2011
IEEE
14 years 1 months ago
A flexible Speech Distortion Weighted Multi-channel Wiener Filter for noise reduction in hearing aids
In this paper, a multi-channel noise reduction algorithm is presented based on a Speech Distortion Weighted Multi-channel Wiener Filter (SDW-MWF) approach that incorporates a fle...
Kim Ngo, Marc Moonen, Søren Holdt Jensen, J...
ENTCS
2010
118views more  ENTCS 2010»
14 years 7 months ago
Fragments-based Model Reduction: Some Case Studies
Molecular biological models usually suffer from a dramatic combinatorial blow up. Indeed, proteins form complexes and can modify each others, which leads to the formation of a hug...
Jérôme Feret
ICML
2004
IEEE
15 years 10 months ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Ting Su, Jennifer G. Dy