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dc.contributor.authorArikan Orhanen_US
dc.description.abstractA recursive algorithm is proposed to obtain an efficient regularized least squares solution to large linear system of equations which arises in many physical measurement models. The algorithm recursively updates the solution in an increasingly larger dimensional subspace whose basis vectors are chosen as a subset of a complete wavelet basis. Robust criterions on how to chose the basis vectors at each iteration, and when to stop the iterations are given.en_US
dc.source.titleProceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysisen_US
dc.subjectComputational complexityen_US
dc.subjectIterative methodsen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMathematical modelsen_US
dc.subjectParameter estimationen_US
dc.subjectRecursive functionsen_US
dc.subjectWavelet transformsen_US
dc.subjectAdaptive partitioning methodsen_US
dc.subjectRecursive reconstruction algorithmen_US
dc.subjectSignal processingen_US
dc.titleWavelet based recursive reconstruction algorithm for linear measurementsen_US
dc.typeConference Paperen_US
dc.publisherIEEE, Piscataway, NJ, United Statesen_US

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