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PR
2006
147views more  PR 2006»
13 years 8 months ago
Robust locally linear embedding
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...
Hong Chang, Dit-Yan Yeung
ICPR
2008
IEEE
14 years 2 months ago
Non-linear feature extraction by linear PCA using local kernel
This paper presents how to extract non-linear features by linear PCA. KPCA is effective but the computational cost is the drawback. To realize both non-linearity and low computati...
Kazuhiro Hotta
ESANN
2003
13 years 9 months ago
Locally Linear Embedding versus Isotop
Abstract. Recently, a new method intended to realize conformal mappings has been published. Called Locally Linear Embedding (LLE), this method can map high-dimensional data lying o...
John Aldo Lee, Cédric Archambeau, Michel Ve...
AUTOMATICA
2010
167views more  AUTOMATICA 2010»
13 years 8 months ago
A new kernel-based approach for linear system identification
This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose s...
Gianluigi Pillonetto, Giuseppe De Nicolao
CVPR
2005
IEEE
14 years 2 months ago
Linear Combination Representation for Outlier Detection in Motion Tracking
In this paper we show that Ullman and Basri’s linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outli...
Guodong Guo, Charles R. Dyer, Zhengyou Zhang