Abstract
The key step in reduced-rank noise reduction algorithms is to approximate a matrix by another one with lower rank, typically by truncating a singular value decomposition (SVD). We give an explicit and closed-form derivation of the filter properties of the rank reduction operation and interpret this operation in the frequency domain by showing that the reduced-rank output signal is identical to that from a filter-bank whose analysis and synthesis filters are determined by the SVD. Our analysis includes the important general case in which pre- and dewhitening is used.
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© 1996 Springer-Verlag Berlin Heidelberg
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Hansen, P.C., Jensen, S.H. (1996). Filter model of reduced-rank noise reduction. In: Waśniewski, J., Dongarra, J., Madsen, K., Olesen, D. (eds) Applied Parallel Computing Industrial Computation and Optimization. PARA 1996. Lecture Notes in Computer Science, vol 1184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62095-8_40
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DOI: https://doi.org/10.1007/3-540-62095-8_40
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