Fast digital locally monotonic regression

N. D. Sidiropoulos

Research output: Contribution to journalConference articlepeer-review


In [1], Restrepo and Bovik developed an elegant mathematical framework in which they studied locally monotonic regressions in RN. The drawback is that the complexity of their algorithms is exponential in N. In this paper, we consider digital locally monotonic regressions, in which the output symbols are drawn from a finite alphabet, and, by making a connection to Viterbi decoding, provide a fast O(|A|2αN) algorithm that computes any such regression, where |A| is the size of the digital output alphabet, α stands for lomo-degree, and N is sample size. This is linear in N, and it renders the technique applicable in practice.

Original languageEnglish (US)
Pages (from-to)217-220
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
StatePublished - Jan 1 1996
EventProceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) - Atlanta, GA, USA
Duration: May 12 1996May 15 1996


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