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» Quantizing Density Estimators
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ICASSP
2010
IEEE
13 years 7 months ago
Empirical quantization for sparse sampling systems
We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is base...
Michael A. Lexa
ICIP
2001
IEEE
14 years 9 months ago
Minimum discrimination information clustering: modeling and quantization with Gauss mixtures
Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximatea large cla...
Robert M. Gray, John C. Young, Anuradha K. Aiyer
CSDA
2006
142views more  CSDA 2006»
13 years 7 months ago
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
: Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its un...
Xibin Zhang, Maxwell L. King, Rob J. Hyndman
IDA
2007
Springer
14 years 1 months ago
DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assign...
Alexander Hinneburg, Hans-Henning Gabriel
CVPR
2005
IEEE
14 years 9 months ago
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine th...
Kai Zhang, Ming Tang, James T. Kwok