Determinant and exchange algorithms for observation subset selection

Authors:
Robert Broughton;Ian Coope;Peter Renaud;Rachael Tappenden
Affiliations:
Mathematics and Statistics Department, University of Canterbury, Christchurch, New Zealand;Mathematics and Statistics Department, University of Canterbury, Christchurch, New Zealand;Mathematics and Statistics Department, University of Canterbury, Christchurch, New Zealand;Mathematics and Statistics Department, University of Canterbury, Christchurch, New Zealand
Venue:
IEEE Transactions on Image Processing
Year:
2010

Citing 8
Cited 0

Detection of Microcalcifications in Mammographies Based on Linear Pixel Prediction and Support-Vector Machines

CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
New results on observation selection in signal reconstruction

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Sequential forward sample selection in array-based image formation

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Orthogonal moments based texture analysis of CT liver images

Pattern Recognition Letters
Degree prediction of malignancy in brain glioma using support vector machines

Computers in Biology and Medicine
Selection of observations in signal reconstruction

IEEE Transactions on Signal Processing
Sequential algorithms for observation selection

IEEE Transactions on Signal Processing
Optimal Sensor Array Configuration in Remote Image Formation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.03

Visualization

Abstract

In many applications involving image reconstruction, signal observation time is limited. This emphasizes the requirement for optimal observation selection algorithms. A selection criterion using the trace of a matrix forms the basis of two existing algorithms, the Sequential Backward Selection and Sequential Forward Selection algorithms. Neither is optimal although both generally perform well. Here we introduce a trace row-exchange criterion to further improve the quality of the selected subset and introduce another observation selection criterion based upon the determinant of a matrix.