Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness ...
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...
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...
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...
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...
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 ...
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...
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...
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...
Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets...