Abstract
Although some of the linear curve fitting techniques provide improvements over the classical least squares fit algorithm, most of them cannot globally stabilize majority of data sets, and are not robust enough to handle moderate levels of noise or missing data. In this paper, we apply “ridge regression regularization” to strengthen the stability and robustness of a linear fitting method, 3L fitting algorithm, while maintaining its Euclidean invariance.
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© 2004 Springer-Verlag Berlin Heidelberg
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Sahin, T., Unel, M. (2004). Globally Stabilized 3L Curve Fitting. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_62
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DOI: https://doi.org/10.1007/978-3-540-30125-7_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
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