Abstract
Ranked models in the form of linear transformations of multivariate feature vectors on a line can be found on the basis of a priori given order within particular pairs of objects or events. Such ranked transformations are designed to preserve given sequential order. In this way, the sequential patterns inside sets of the feature vectors can be discovered and modelled. Attention is paid here to combining problems of sequential patterns modelling and recognition with feature selection. The feature selection problem is aimed at the best representation of the sequential patterns. The convex and piecewise linear (CPL) criterion functions are used here both for designing ranked linear models and for feature selection.
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This work was partially supported by the grants KBN 3T11F011 30, by the grant W/WI/1/2007from the Białystok University of Technology and by the grant 16/St/2007 from the Institute of Biocybernetics and Biomedical Engineering PAS.
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Bobrowski, L. Ranked linear models and sequential patterns recognition. Pattern Anal Applic 12, 1–7 (2009). https://doi.org/10.1007/s10044-007-0092-8
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DOI: https://doi.org/10.1007/s10044-007-0092-8