Abstract
Blind source separation is commonly based on maximizing measures related to independence of estimated sources such as mutual statistical independence assuming non-Gaussian distributions, decorrelation at different time-lags assuming spectral differences or decorrelation assuming source non-stationarity.
Here, the use of an alternative model for source separation is explored which is based on the assumption that sources emit signal energy at mutually different times. In the limiting case, this corresponds to only a single source being “active” at each point in time, resulting in mutual disjointness of source signal supports and negative mutual correlations of source signal envelopes. This assumption will not be fulfilled perfectly for real signals, however, by maximizing disjointness of estimated sources (under a linear mixing/demixing model) we demonstrate that source separation is nevertheless achieved when this assumptions is only partially fulfilled.
The conceptual benefits of the disjointness assumption are that (1) in certain applications it may be desirable to explain observed data in terms of mutually disjoint “parts” and (2) the method presented here preserves the special physical information assigned to amplitude zero of a signal which corresponds to the absence of energy (rather than subtracting the signal mean prior to analysis which for non zero-mean sources destroys this information).
The method of disjoint component analysis (DCA) is derived and it is shown that its update equations bear remarkable similarities with maximum likelihood independent component analysis (ICA). Sources with systematically varied degrees of disjointness are constructed and processed by DCA and Infomax and Jade ICA. Results illustrate the behaviour of DCA and ICA under these regimes with two main results: (1) DCA leads to a higher degree of separation than ICA, (2) DCA performs particularly well on positive-valued sources as long as they are at least moderately disjoint, and (3) The performance peak of ICA for zero-mean sources is achieved when sources are disjoint (but not independent).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amari, S.-I.: Natural gradient works efficiently in learning. Neural Computation 10, 251–276 (1998)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Benharrosh, M.S., Takerkart, S., Cohen, J.D., Daubechies, I.C., Richter, W.: Using ICA on fMRI: Does independence matter?. In: Human Brain Mapping, abstract no. 784 (2003)
Cardoso, J.-F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEE Proceedings–F 140, 362–370 (1993)
Donoho, D., Elad, M.: Optimally sparse representation in general (nonorthogonal) dictionaries via l 1 minimization. Proc. Nat. Acad. Sci. 100, 2197–2202 (2003)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative Matrix Factorization. Nature 40, 788–791 (1999)
Makeig, S., et al.: EEGLAB: ICA toolbox for psychophysical research, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego (2000), http://www.sccn.ucsd.edu/eeglab
Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. Advances in neural information processing system 8, 145–151 (1996)
Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Research 37, 3311–3325 (1997)
Rickard, S., Yilmaz, Z.: On the approximate w-disjoint orthogonality of speech. In: ICASSP 2002, pp. I–529–I–532 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Anemüller, J. (2007). Maximization of Component Disjointness: A Criterion for Blind Source Separation. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)