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

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 con...

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Vydáno v:Conference record - Asilomar Conference on Signals, Systems, & Computers s. 1743 - 1747
Hlavní autoři: Ramirez, David, Santamaria, Ignacio, Van Vaerenbergh, Steven, Scharf, Louis L.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2018
Témata:
Convergence
Covariance matrices
Expectation-Maximization
Factor Analysis
Load modeling
Loading
Maximum Likelihood
Maximum likelihood estimation
MIMO channels
MIMO communication
Multivariate Normal Model
Optimization
ISSN:2576-2303
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Popis
Shrnutí: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.
ISSN:2576-2303
DOI:10.1109/ACSSC.2018.8645457