An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors

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URI: http://hdl.handle.net/10902/15871
ISBN: 978-1-5386-9218-9
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Autoría
Ramírez García, David; Santamaría Caballero, Luis IgnacioAutoridad Unican; Vaerenbergh, Steven vanAutoridad Unican; Scharf, Louis L.Autoridad Unican
Fecha
2018
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© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publicado en
52nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2018, 1743-1747
Editorial
IEEE
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Palabras clave
Factor analysis
Expectation-maximization
Maximum likelihood
MIMO channels
Multivariate normal model
Resumen/Abstract
An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.
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