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Istituto di Scienza e Tecnologie dell'Informazione     
Kuruoglu E. E., Theis F. Editorial - Dependent component analysis. In: Eurasip Journal on Advances in Signal Processing. Editorial, vol. 2013 (185) pp. 1 - 2. E.E. Kuruoglu, F. Theis (eds.). Springer, 2013.
 
 
Abstract
(English)
In this special issue, we aimed to provide a view of the general panorama of the research on dependent component analysis. The issue starts with a paper by Castella et al., who demonstrate that for certain classes of dependent sources, classical ICA methods still apply, and who also give explicit conditions for this situation. Next comes a methodological paper by Caiafa, which provides the citerion for choosing valid objective functions for independent and dependent source separation. The following algorithmic paper is on complex independent vector analysis by Shen and Kleinsteuber and presents non-unitary matrix diagolisation methods. Na and Yu introduce a method utilising subspace and subband non-linearity for independent vector analysis. Four application-oriented papers follow: Quiros and Wilson provide a Bayesian formulation for the separation of dependent sources in astrophysical images using a mixture prior. Tonazzini and Bedini provide results on document image restoration using correlated component analysis. Almeida et al. study the separation of phase-locked sources in MEG data. Liang and Chambers provide a separation method based on independent vector analysis for multimedia data.
URL: http://asp.eurasipjournals.com/content/2013/1/185
DOI: 10.1186/1687-6180-2013-185
Subject Dependent component analysis
Independent vector analysis
Independent subspace analysis
Source separation
I.5 PATTERN RECOGNITION
G.3 PROBABILITY AND STATISTICS. Multivariate statistics
I.5.4 Applications. Signal processing
94A12 Signal theory


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