Skip to main content
SpringerLink
Log in
Book cover

International Conference on Latent Variable Analysis and Signal Separation

LVA/ICA 2010: Latent Variable Analysis and Signal Separation pp 612–619Cite as

Semi-nonnegative Independent Component Analysis: The (3,4)-SENICAexp Method

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6365))

Abstract

To solve the Independent Component Analysis (ICA) problem under the constraint of nonnegative mixture, we propose an iterative algorithm, called (3,4)-SENICAexp. This method profits from some interesting properties enjoyed by third and fourth order statistics in the presence of mixed independent processes, imposing the nonnegativity of the mixture by means of an exponential change of variable. This process allows us to obtain an unconstrained problem, optimized using an ELSALS-like procedure. Our approach is tested on synthetic magnetic resonance spectroscopic imaging data and compared to two existing ICA methods, namely SOBI and CoM2.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Herault, J., Jutten, C., Ans, B.: Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimétique en apprentissage non supervisé. In: GRETSI 1885, Dixième colloque sur le Traitement du Signal et des Images, Nice, France, pp. 1017–1022 (September 1985)

    Google Scholar 

  2. Comon, P.: Component Analysis, a new concept ? Signal Processing 36(3), 287–314 (1994)

    Article  MATH  Google Scholar 

  3. Comon, P., Jutten, C. (eds.): Handbook of Blind Source Separation, Independent Component Analysis and Applications. Academic Press, London (2010) ISBN: 978-0-12-374726-6, hal-00460653

    Google Scholar 

  4. Ladroue, C., Howe, F., Griffiths, J., Tate, R.: Independent component analysis for automated decomposition of in vivo magnetic resonance spectra. Magnetic Resonnance in Medicine 50, 697–703 (2003)

    Article  Google Scholar 

  5. Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Transactions On Signal Processing 45(2), 434–444 (1997)

    Article  Google Scholar 

  6. Albera, L., Ferreol, A., Comon, P., Chevalier, P.: Blind Identification of Overcomplete Mixtures of sources (BIOME). Linear Algebra Applications 391C, 3–30 (2004)

    Article  MathSciNet  Google Scholar 

  7. Sajda, P., Du, S., Brown, T., Stoyanova, R., Shungu, D., Mao, X., Parra, L.: Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain. IEEE Transactions on Medical Imaging 23(12), 1453–1465 (2004)

    Article  Google Scholar 

  8. Berry, M., Browne, M., Langville, A.N., Pauca, P., Plemmons, R.J.: Algorithms and aplications for approximate nonnegative matrix factorization. Computational Statistics and Data Analysis 52(8), 155–173 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  9. Merritt, M., Zhang, Y.: Interior-point gradient method for large-scale totally nonnegative least squares problems. Journal of Optimization Theory and Application 126, 191–202 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  10. Chu, M., Diele, F., Plemmons, R., Ragni, S.: Optimality, computation and interpretation of nonnegative matrix factorizations, http://www.wfu.edu/~plemmons (2004)

  11. Moussaoui, S., Brie, D., Mohammad-Djafari, A., Carteret, C.: Separation of non-negative mixture of non-negative sources using a bayesian approach and mcmc sampling. IEEE Trans. Signal Processing 54(11), 4133–4145 (2006)

    Article  Google Scholar 

  12. Oja, E., Plumbley, M.: Blind separation of positive sources by globally convergent gradient search. Neural Computation 16(9), 1811–1825 (2004)

    Article  MATH  Google Scholar 

  13. Zheng, C.H., Huang, D.S., Sun, Z.L., Lyu, M., Lok, T.M.: Nonnegative independent component analysis based on minimizing mutual information technique. Neurocomputing 69(7), 878–883 (2006)

    Article  Google Scholar 

  14. Comon, P., Luciani, X., Almeida, A.L.F.D.: Tensor decompositions, alternating least squares and other thales. Journal of Chemometrics 23 (April 2009)

    Google Scholar 

  15. Bro, R.: Multi-way Analysis in the Food Industry: Models, Algorithms, and Applications. PhD thesis, University of Amsterdam (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inserm, UMR 642, Rennes, F-35000, France

    Julie Coloigner, Laurent Albera, Amar Kachenoura & Lotfi Senhadji

  2. Université de Rennes 1, LTSI, Rennes, F-35000, France

    Julie Coloigner, Laurent Albera, Amar Kachenoura & Lotfi Senhadji

  3. CNRS, UMR 6070, Sophia Antipolis, F-06903, France

    Pierre Comon

  4. I3S, Université de Nice Sophia Antipolis, F-06903, France

    Pierre Comon

  5. Faculty of Mechanical and Electrical Engineering, University Al-Baath, Homs, Syria

    Ahmad Karfoul

Authors
  1. Julie Coloigner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurent Albera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmad Karfoul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amar Kachenoura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pierre Comon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lotfi Senhadji
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Electrical Engineering, Universitè d’Evry Val d’Essone, 40 rue du Pelvoux, 91020, Courcouronnes, France

    Vincent Vigneron

  2. Laboratoire I3S, Les Algorithmes - Euclide-B, BP 121, Université de Nice-Sophia Antipolis, 2000 Route des Lucioles, 06903, Sophia Antipolis Cedex, France

    Vicente Zarzoso

  3. School of Engineering, Dept. of Telecommunications, ISITSchool of Engineering, Dept. of Telecommunications, ISITV, Université de Toulon, Avenue George Pompidou, BP 56, La Valette du Var, Cedex, 83162, France

    Eric Moreau

  4. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes cedex, France

    Rémi Gribonval

  5. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    Emmanuel Vincent

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Coloigner, J., Albera, L., Karfoul, A., Kachenoura, A., Comon, P., Senhadji, L. (2010). Semi-nonnegative Independent Component Analysis: The (3,4)-SENICAexp Method. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_76

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15995-4_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15994-7

  • Online ISBN: 978-3-642-15995-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation