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ECCV
2004
Springer
16 years 3 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»
15 years 2 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 5 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 10 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
16 years 2 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