Fast Digital Locally Monotonic Regression

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dc.contributor.authorSidiropoulos, N.D.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:59:35Z
dc.date.available2007-05-23T09:59:35Z
dc.date.issued1995en_US
dc.description.abstractIn [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|2 aN) algorithm that computes any such regression, where |A| is the size of the digital output alphabet, a stands for lomo-degree, and N is sample size. This is linear in N , and it renders the technique applicable in practice.en_US
dc.format.extent543496 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5663
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1995-80en_US
dc.subjectnonlinear filteringen_US
dc.subjectlocal monotonicityen_US
dc.subjectprinciple of optimalityen_US
dc.subjectviterbi algorithmen_US
dc.subjectestimationen_US
dc.subjectfilteringen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleFast Digital Locally Monotonic Regressionen_US
dc.typeTechnical Reporten_US

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