Application of piece-wise regression to detecting internal structure of signal

Victor L. Brailovsky; Yulia Kempner

doi:10.1016/0031-3203(92)90148-C

Application of piece-wise regression to detecting internal structure of signal

Victor L. Brailovsky^*, Yulia Kempner

^*Corresponding author for this work

School of Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

The problem of restoring the underlying structure of a signal with the help of piece-wise regression is considered. The case of interest is that the domain of definition of a response function consists of a number of regions of smoothness. The number of regions and the location of change points are not fixed in advance and they should be found by analyzing the signal corrupted with noise. For a given number of smooth regions the best piece-wise regression may be found with the help of an approach based on dynamic programming. The selection of the best model (the best number of regions of smoothness) is performed with the help of a probabilistic estimate. Some properties of the estimate and the whole procedure are studied and the results of experiments are presented.

Original language	English
Pages (from-to)	1361-1370
Number of pages	10
Journal	Pattern Recognition
Volume	25
Issue number	11
DOIs	https://doi.org/10.1016/0031-3203(92)90148-C
State	Published - Nov 1992

Keywords

Dynamic programming
Monte-Carlo method
Piece-wise regression
Probabilistic estimate
V-C dimension

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10.1016/0031-3203(92)90148-C

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Application of piece-wise regression to detecting internal structure of signal

Abstract

Keywords

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