Multilinear Karhunen-Loève Transforms

Many recent applications involve distributed signal processing where a source signal is observed by, say, p local receiver-transmitters and then transmitted to a reconstruction center for the signal estimation. An optimal determination of the receiver-transmitters and the reconstruction center requi...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2022, Vol.70, p.5148-5163
Main Authors: Howlett, Phil, Torokhti, Anatoli, Pudney, Peter, Soto-Quiros, Pablo
Format: Article
Language:English
Subjects:
Computing costs
Covariance matrices
Estimation
Image reconstruction
Karhunen-Loève transform
Loss measurement
Matrix decomposition
Optimization
Principal component analysis
Reconstruction
Signal processing
Transforms
Transmitters
Wiener filtering
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Summary:Many recent applications involve distributed signal processing where a source signal is observed by, say, p local receiver-transmitters and then transmitted to a reconstruction center for the signal estimation. An optimal determination of the receiver-transmitters and the reconstruction center requires extensions of the Karhunen-Loève transform (KLT) and Wiener filter. In this paper, the associated extensions are provided. The proposed optimal multilinear filter is a generalization of the Wiener filter and consists of p terms where each term is associated with a local receiver-transmitter. For the case when the receiver-transmitters must reduce the dimensionality of the observed signals, two associated techniques are proposed: the multilinear KLT-1 and multilinear KLT-2. The multilinear KLT-1 is constructed in terms of pseudo-inverse matrices and therefore always exists. The multilinear KLT-2 is given in terms of non-singular matrices and it may provide a higher associated accuracy than that of the multilinear KLT-1. All three proposed techniques are based on a reduction of the original problem to p separate error minimization problems with small matrices. This allows us to provide a fast computational procedure for the multilinear filter, and decrease the computational cost for constructing the multilinear KLT-1 and KLT-2.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2022.3214684