Lie Group Methods in Blind Signal Processing.
Independent Component Analysis
Lie algebra
Lie groups
geometric optimization
independent subspace analysis
sensors
toral subalgebra
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
13 Jan 2020
13 Jan 2020
Historique:
received:
31
10
2019
revised:
27
12
2019
accepted:
07
01
2020
entrez:
17
1
2020
pubmed:
17
1
2020
medline:
17
1
2020
Statut:
epublish
Résumé
This paper deals with the use of Lie group methods to solve optimization problems in blind signal processing (BSP), including Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). The paper presents the theoretical fundamentals of Lie groups and Lie algebra, the geometry of problems in BSP as well as the basic ideas of optimization techniques based on Lie groups. Optimization algorithms based on the properties of Lie groups are characterized by the fact that during optimization motion, they ensure permanent bonding with a search space. This property is extremely significant in terms of the stability and dynamics of optimization algorithms. The specific geometry of problems such as ICA and ISA along with the search space homogeneity enable the use of optimization techniques based on the properties of the Lie groups O ( n ) and S O ( n ) . An interesting idea is that of optimization motion in one-parameter commutative subalgebras and toral subalgebras that ensure low computational complexity and high-speed algorithms.
Identifiants
pubmed: 31941069
pii: s20020440
doi: 10.3390/s20020440
pmc: PMC7013945
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Références
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pubmed: 24808148