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» Density Estimation: Nonparametric Techniques
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CSDA
2008
94views more  CSDA 2008»
13 years 8 months ago
Feature significance for multivariate kernel density estimation
Multivariate kernel density estimation provides information about structure in data. Feature significance is a technique for deciding whether features
Tarn Duong, Arianna Cowling, Inge Koch, M. P. Wand
CVPR
2007
IEEE
14 years 11 months ago
A Graph Cut Approach to Image Segmentation in Tensor Space
This paper proposes a novel method to apply the standard graph cut technique to segmenting multimodal tensor valued images. The Riemannian nature of the tensor space is explicitly...
Allen Tannenbaum, James G. Malcolm, Yogesh Rathi
SDM
2009
SIAM
144views Data Mining» more  SDM 2009»
14 years 6 months ago
CORE: Nonparametric Clustering of Large Numeric Databases.
Current clustering techniques are able to identify arbitrarily shaped clusters in the presence of noise, but depend on carefully chosen model parameters. The choice of model param...
Andrej Taliun, Arturas Mazeika, Michael H. Bö...
ICCV
2011
IEEE
12 years 9 months ago
A Nonparametric Riemannian Framework on Tensor Field with Application to Foreground Segmentation
Background modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent resear...
Rui Caseiro, João F. Henriques, Pedro Martins, Jo...
ICIP
2005
IEEE
14 years 10 months ago
Nonparametric clustering using quantum mechanics
This paper introduces a new nonparametric estimation approach that can be used for data that is not necessarily Gaussian distributed. The proposed approach employs the Shr?odinger...
Nikolaos Nasios, Adrian G. Bors