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ICPR
2006
IEEE
14 years 7 months ago
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to se...
Marco Cristani, Umberto Castellani, Vittorio Murin...
ACCV
2009
Springer
14 years 1 months ago
Evolving Mean Shift with Adaptive Bandwidth: A Fast and Noise Robust Approach
Abstract. This paper presents a novel nonparametric clustering algorithm called evolving mean shift (EMS) algorithm. The algorithm iteratively shrinks a dataset and generates well ...
Qi Zhao, Zhi Yang, Hai Tao, Wentai Liu
IJCAI
2007
13 years 8 months ago
Collapsed Variational Dirichlet Process Mixture Models
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of ...
Kenichi Kurihara, Max Welling, Yee Whye Teh
BMCBI
2007
138views more  BMCBI 2007»
13 years 6 months ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...
CSDA
2007
151views more  CSDA 2007»
13 years 6 months ago
Robust semiparametric mixing for detecting differentially expressed genes in microarray experiments
An important goal of microarray studies is the detection of genes that show significant changes in observed expressions when two or more classes of biological samples such as tre...
Marco Alfò, Alessio Farcomeni, Luca Tardell...