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TIP
2010
162views more  TIP 2010»
9 years 8 months ago
Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty
Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness ...
A. K. Qin, David A. Clausi
SIGPRO
2011
229views Hardware» more  SIGPRO 2011»
9 years 8 months ago
Fast and exact synthesis of stationary multivariate Gaussian time series using circulant embedding
A fast and exact procedure for the numerical synthesis of stationary multivariate Gaussian time series with a priori prescribed and well controlled autoand cross-covariance functi...
Hannes Helgason, Vladas Pipiras, Patrice Abry
ML
2002
ACM
111views Machine Learning» more  ML 2002»
10 years 1 months ago
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
Binningandtruncationofdataarecommonindataanalysisandmachinelearning.Thispaperaddresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM ap...
Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachl...
JC
2000
138views more  JC 2000»
10 years 1 months ago
Multivariate Polynomials, Duality, and Structured Matrices
We rst review thebasic properties of the well knownclasses of Toeplitz, Hankel, Vandermonde, and other related structured matrices and reexamine their correlation to operations wi...
Bernard Mourrain, Victor Y. Pan
CSDA
2007
159views more  CSDA 2007»
10 years 1 months ago
Multivariate out-of-sample tests for Granger causality
A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in th...
Sarah Gelper, Christophe Croux
BMCBI
2006
122views more  BMCBI 2006»
10 years 1 months ago
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifi...
Carmen Lai, Marcel J. T. Reinders, Laura J. van't ...
BMCBI
2006
122views more  BMCBI 2006»
10 years 1 months ago
A multivariate prediction model for microarray cross-hybridization
Background: Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental proble...
Yian A. Chen, Cheng-Chung Chou, Xinghua Lu, Elizab...
CSDA
2010
98views more  CSDA 2010»
10 years 1 months ago
Design-based estimation for geometric quantiles with application to outlier detection
Geometric quantiles are investigated using data collected from a complex survey. Geometric quantiles are an extension of univariate quantiles in a multivariate set-up that uses th...
Mohamed Chaouch, Camelia Goga
BMCBI
2008
121views more  BMCBI 2008»
10 years 1 months ago
Stability of gene contributions and identification of outliers in multivariate analysis of microarray data
Background: Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages com...
Florent Baty, Daniel Jaeger, Frank Preiswerk, Mart...
ICASSP
2010
IEEE
10 years 1 months ago
Swift: Scalable weighted iterative sampling for flow cytometry clustering
Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets...
Iftekhar Naim, Suprakash Datta, Gaurav Sharma, Jam...
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