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A novel algorithm for computing rank reduced matrix approximations

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2 Author(s)
Manton, J.H. ; Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia ; Hua, Y.

The problem of approximating a matrix by one of lower rank occurs in a number of signal processing problems, including reduced rank Wiener filtering and reduced rank maximum likelihood estimation. This paper derives a novel algorithm for computing the optimal rank-reduced approximation of a given matrix under an arbitrary weighted norm. Simulations demonstrate the advantages of the algorithm over the traditional alternating projections algorithm

Published in:

Wireless Communications, 2001. (SPAWC '01). 2001 IEEE Third Workshop on Signal Processing Advances in

Date of Conference:

2001