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» Robust principal component analysis
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ICPR
2002
IEEE
14 years 10 months ago
PCA in Autocorrelation Space
The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the au...
Vlad Popovici, Jean-Philippe Thiran
ICPR
2002
IEEE
14 years 10 months ago
Manifold Pursuit: A New Approach to Appearance Based Recognition
Manifold Pursuit (MP) extends Principal Component Analysis to be invariant to a desired group of image-plane transformations of an ensemble of un-aligned images. We derive a simpl...
Amnon Shashua, Anat Levin, Shai Avidan
MICCAI
2003
Springer
14 years 9 months ago
Statistical Shape Modeling of Unfolded Retinotopic Maps for a Visual Areas Probabilistic Atlas
Abstract. This paper proposes a statistical model of functional landmarks delimiting low level visual areas which are highly variable across individuals. Low level visual areas are...
Isabelle Corouge, Michel Dojat, Christian Barillot
ICML
2009
IEEE
14 years 9 months ago
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
Linli Xu, Martha White, Dale Schuurmans
ICML
2007
IEEE
14 years 9 months ago
Local learning projections
This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essen...
Bernhard Schölkopf, Kai Yu, Mingrui Wu, Shipe...