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@article{1468976,
author = {Kraus, David and Stefanucci, Marco},
article_location = {Oxford},
article_number = {1},
doi = {http://dx.doi.org/10.1093/biomet/asy060},
keywords = {Classification; Conjugate gradients; Domain selection; Functional data; Partial observation; Regularization; Ridge method},
language = {eng},
issn = {0006-3444},
journal = {Biometrika},
title = {Classification of functional fragments by regularized linear classifiers with domain selection},
url = {https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7},
volume = {106},
year = {2019}
}
TY - JOUR
ID - 1468976
AU - Kraus, David - Stefanucci, Marco
PY - 2019
TI - Classification of functional fragments by regularized linear classifiers with domain selection
JF - Biometrika
VL - 106
IS - 1
SP - 161-180
EP - 161-180
PB - Oxford Univ Press
SN - 00063444
KW - Classification
KW - Conjugate gradients
KW - Domain selection
KW - Functional data
KW - Partial observation
KW - Regularization
KW - Ridge method
UR - https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7
L2 - https://academic.oup.com//biomet/advance-article/doi/10.1093/biomet/asy060/5250873?guestAccessKey=64dbd3b6-5ffc-4dee-8586-3fee35ebded7
N2 - We consider classification of functional data into two groups by linear classifiers based on one-dimensional projections of functions. We reformulate the task of finding the best classifier as an optimization problem and solve it by the conjugate gradient method with early stopping, the principal component method, and the ridge method. We study the empirical version with finite training samples consisting of incomplete functions observed on different subsets of the domain and show that the optimal, possibly zero, misclassification probability can be achieved in the limit along a possibly nonconvergent empirical regularization path. We propose a domain extension and selection procedure that finds the best domain beyond the common observation domain of all curves. In a simulation study we compare the different regularization methods and investigate the performance of domain selection. Our method is illustrated on a medical dataset, where we observe a substantial improvement of classification accuracy due to domain extension.
ER -
KRAUS, David and Marco STEFANUCCI. Classification of functional fragments by regularized linear classifiers with domain selection. \textit{Biometrika}. Oxford: Oxford Univ Press, 2019, vol.~106, No~1, p.~161-180. ISSN~0006-3444. Available from: https://dx.doi.org/10.1093/biomet/asy060.
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