Exploring the linearity of models on the basis of ranked data

Authors:
Leon Bobrowski;Ralph C. Huntsinger
Affiliations:
Bialystok Technical University, Poland and Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland;Bialystok Technical University, Poland and California State University-Chico and Humboldt State University, California and Lomza State College of Computer Science and Business Administration, Pola ...
Venue:
Proceedings of the 2007 Summer Computer Simulation Conference
Year:
2007

Citing 4
Cited 1

Applied multivariate statistical analysis

Applied multivariate statistical analysis
Design of piecewise linear classifiers from formal neurons by a basis exchange technique

Pattern Recognition
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Ranked modelling with feature selection based on the CPL criterion functions

MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition

CPL Clustering with Feature Costs

ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects

Quantified Score

Hi-index	0.00

Visualization

Abstract

The ranked data are based on two sources of information: on results of measurements that are represented as a set of n-dimensional feature vectors, and on ranking relations between some of these vectors. The ranked data can be modelled in the form of a linear transformation from the feature space on a line. The ranked transformation preserves as much as possible the ranking relations between feature vectors. The linear ranked transformations could be explored on the basis of the linear separability of the differential vectors. A linear transformation can fully reflect the ranking relations if the feature vectors are linearly independent.