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ICANN
2007
Springer
14 years 4 months ago
Selection of Basis Functions Guided by the L2 Soft Margin
Support Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, th...
Ignacio Barrio, Enrique Romero, Lluís Belan...
PAMI
2000
142views more  PAMI 2000»
13 years 9 months ago
Evolutionary Pursuit and Its Application to Face Recognition
Abstract-- This paper introduces Evolutionary Pursuit (EP) as a novel and adaptive representation method for image encoding and classification. In analogy to projection pursuit met...
Chengjun Liu, Harry Wechsler
ADCM
2010
85views more  ADCM 2010»
13 years 10 months ago
Stability of kernel-based interpolation
It is often observed that interpolation based on translates of radial basis functions or non-radial kernels is numerically unstable due to exceedingly large condition of the kerne...
Stefano De Marchi, Robert Schaback
ICPR
2002
IEEE
14 years 11 months ago
Rotation and Scale Invariant Shape Representation and Recognition Using Matching Pursuit
Using a low-level representation of images, like matching pursuit, we introduce a new way of describing objects through a general description using a translation, rotation, and is...
François Mendels, Pierre Vandergheynst, Jea...
ICASSP
2009
IEEE
14 years 4 months ago
Separation of a subspace-sparse signal: Algorithms and conditions
In this paper, we show how two classical sparse recovery algorithms, Orthogonal Matching Pursuit and Basis Pursuit, can be naturally extended to recover block-sparse solutions for...
Arvind Ganesh, Zihan Zhou, Yi Ma