Title :
Computing the reduced rank Wiener filter by IQMD
Author :
Hua, Yingbo ; Nikpour, Maziar
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
Abstract :
The reduced-rank Wiener filter (RRWF) is a generic tool for data compression and filtering. This letter presents an iterative quadratic minimum distance (IQMD) algorithm for computing the RRWF. Although it is iterative in nature, the IQMD algorithm is shown to be globally and exponentially convergent under some weak conditions. While the conventional algorithms for computing the RRWF require an order of n/sup 3/ flops, the IQMD algorithm requires only an order of n/sup 2/ flops at each iteration where n is the dimension of data. The number of iterations required in practice is often small due to the exponential convergence rate of the IQMD.
Keywords :
Karhunen-Loeve transforms; Wiener filters; convergence of numerical methods; data compression; filtering theory; iterative methods; IQMD algorithm; Karhunen-Loeve transform; data compression; exponential convergence; filtering; generic tool; global convergence; iterative quadratic minimum distance algorithm; reduced rank Wiener filter; Closed-form solution; Convergence; Data compression; Filtering; Iterative algorithms; Karhunen-Loeve transforms; Signal processing; Singular value decomposition; Wiener filter;
Journal_Title :
Signal Processing Letters, IEEE