NDLI: Decompositional independent component analysis using multi-objective optimization

Content Provider	Springer Nature Link
Author	Goh, Sim Kuan Abbass, Hussein A. Tan, Kay Chen Al Mamun, Abdullah
Copyright Year	2015
Abstract	Current approaches for blind source separation, such as independent component analysis (ICA), implicitly assume that the number of collected signals equals the number of sources. This assumption does not hold true in many real-world applications as in the case of electroencephalographic (EEG) data collected from the surface of a human’s scalp, where independent EEG information is mixed with independent artifacts. This situation is abstracted in this paper by introducing the singers’ party problem, where the number of signals collected from the party equals the number of singers. However, there are also a number of instruments playing at the party representing independent sources that need to be removed correctly to extract the voices of the singers. In this paper, we introduce a decompositional approach to project the sources found in ICA into a higher-dimensional space; providing the ability to separate local (singers) information from shared/global (instruments) information. The decomposition will also associate each component with a mixed signal, creating a bijective relationship between the mixed signals and the sources. The problem is formulated as a multi-objective optimization problem. We compare the pros and cons of two different multi-objective formulations of the problem and demonstrate that one of the formulations can effectively solve the singers party problem.
Starting Page	1289
Ending Page	1304
Page Count	16
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	4
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-01-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Electroencephalography Independent component analysis Multi-objective optimization Singers party problem Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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