Skip to main content
SpringerLink
Log in

Extended Sparse Nonnegative Matrix Factorization

  • Conference paper
Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

Included in the following conference series:

Abstract

In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.

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

Access this chapter

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 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 5, 1457–1469 (2004)

    MathSciNet  Google Scholar 

  2. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 40, 788–791 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Biophysics, University of Regensburg, 93040, Regensburg, Germany

    Kurt Stadlthanner, Fabian J. Theis & Elmar W. Lang

  2. Dept. Arq. y Téc. de Comp., Universidad de Granada, 18071, Granada, Spain

    Carlos G. Puntonet

Authors
  1. Kurt Stadlthanner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabian J. Theis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos G. Puntonet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elmar W. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Stadlthanner, K., Theis, F.J., Puntonet, C.G., Lang, E.W. (2005). Extended Sparse Nonnegative Matrix Factorization. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_31

Download citation

  • DOI: https://doi.org/10.1007/11494669_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation