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ECML
2007
Springer
15 years 8 months ago
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Tapani Raiko, Alexander Ilin, Juha Karhunen
ICASSP
2011
IEEE
14 years 6 months ago
Estimation of cortical connectivity from E/MEG using nonlinear state-space models
We present the problem of estimating cortical connectivity between different regions of the cortex from scalp electroencephalographic (EEG) or magnetoencephalographic (MEG) data a...
Bing Leung, Patrick Cheung, Barry D. Van Veen
MICCAI
2007
Springer
16 years 3 months ago
Shape Analysis Using a Point-Based Statistical Shape Model Built on Correspondence Probabilities
A fundamental problem when computing statistical shape models is the determination of correspondences between the instances of the associated data set. Often, homologies between po...
Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Heinz...
EOR
2006
97views more  EOR 2006»
15 years 2 months ago
Bayesian portfolio selection with multi-variate random variance models
We consider multi-period portfolio selection problems for a decision maker with a specified utility function when the variance of security returns is described by a discrete time ...
Refik Soyer, Kadir Tanyeri
ANOR
2002
57views more  ANOR 2002»
15 years 2 months ago
Redefining Event Variables for Efficient Modeling of Continuous-Time Batch Processing
Abstract: We define events so as to reduce the number of events and decision variables needed for modeling batchscheduling problems such as described in [Westenberger and Kallrath ...
Siqun Wang, Monique Guignard