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• W2951762010 abstract "This paper presents two novel regularization methods motivated in part by the geometric significance of biorthogonal bases in signal processing applications. These methods, in particular, draw upon the structural relevance of orthogonality and biorthogonality principles and are presented from the perspectives of signal processing, convex programming, continuation methods and nonlinear projection operators. Each method is specifically endowed with either a homotopy or tuning parameter to facilitate tradeoff analysis between accuracy and numerical stability. An example involving a basis comprised of real exponential signals illustrates the utility of the proposed methods on an ill-conditioned inverse problem and the results are compared to standard regularization techniques from the signal processing literature." @default.
• W2951762010 created "2019-06-27" @default.
• W2951762010 creator A5012905461 @default.
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• W2951762010 date "2015-09-19" @default.
• W2951762010 modified "2023-09-27" @default.
• W2951762010 title "Trading Accuracy for Numerical Stability: Orthogonalization, Biorthogonalization and Regularization" @default.
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