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- W2114748345 abstract "We present theoretical results pertaining to the ability of lscr <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> minimization to recover sparse and compressible signals from incomplete and noisy measurements. In particular, we extend the results of Candes, Romberg and Tao (2005) to the p < 1 case. Our results indicate that depending on the restricted isometry constants (see, e.g., Candes and Tao (2006; 2005)) and the noise level, lscr <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</sub> minimization with certain values of p < 1 provides better theoretical guarantees in terms of stability and robustness than lscr <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> minimization does. This is especially true when the restricted isometry constants are relatively large." @default.
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- W2114748345 date "2008-03-01" @default.
- W2114748345 modified "2023-10-16" @default.
- W2114748345 title "Stable sparse approximations via nonconvex optimization" @default.
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- W2114748345 doi "https://doi.org/10.1109/icassp.2008.4518502" @default.
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