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Latent Variable Analysis and Signal Separation

10th International Conference, LVA/ICA 2012, Tel Aviv, Israel, March 12-15, 2012, Proceedings

  • Conference proceedings
  • © 2012

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Editors:
  1. Fabian Theis
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  2. Andrzej Cichocki
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  3. Arie Yeredor
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  4. Michael Zibulevsky
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7191)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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Conference proceedings info: LVA/ICA 2012.

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Table of contents (65 papers)

  1. Front Matter

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  2. General LVA/ICA Theory, Methods and Extensions

    1. Block Component Analysis, a New Concept for Blind Source Separation

      • Lieven De Lathauwer
      Pages 1-8

    2. Partially Linear Estimation with Application to Image Deblurring Using Blurred/Noisy Image Pairs

      • Tomer Michaeli, Daniel Sigalov, Yonina C. Eldar
      Pages 9-16

    3. Causal Discovery for Linear Non-Gaussian Acyclic Models in the Presence of Latent Gaussian Confounders

      • Zhitang Chen, Laiwan Chan
      Pages 17-24

    4. Alleviating the Influence of Weak Data Asymmetries on Granger-Causal Analyses

      • Stefan Haufe, Vadim V. Nikulin, Guido Nolte
      Pages 25-33

    5. Online PLCA for Real-Time Semi-supervised Source Separation

      • Zhiyao Duan, Gautham J. Mysore, Paris Smaragdis
      Pages 34-41

    6. Cramér-Rao Bound for Circular Complex Independent Component Analysis

      • Benedikt Loesch, Bin Yang
      Pages 42-49

    7. Complex Non-Orthogonal Joint Diagonalization Based on LU and LQ Decompositions

      • Ke Wang, Xiao-Feng Gong, Qiu-Hua Lin
      Pages 50-57

    8. Exact and Approximate Quantum Independent Component Analysis for Qubit Uncoupling

      • Yannick Deville, Alain Deville
      Pages 58-65

    9. A Matrix Joint Diagonalization Approach for Complex Independent Vector Analysis

      • Hao Shen, Martin Kleinsteuber
      Pages 66-73

    10. Algebraic Solutions to Complex Blind Source Separation

      • Hao Shen, Martin Kleinsteuber
      Pages 74-81

    11. On the Separation Performance of the Strong Uncorrelating Transformation When Applied to Generalized Covariance and Pseudo-covariance Matrices

      • Arie Yeredor
      Pages 82-90

    12. A Canonical Correlation Analysis Based Method for Improving BSS of Two Related Data Sets

      • Juha Karhunen, Tele Hao, Jarkko Ylipaavalniemi
      Pages 91-98

    13. A Probability-Based Combination Method for Unsupervised Clustering with Application to Blind Source Separation

      • Julian Mathias Becker, Martin Spiertz, Volker Gnann
      Pages 99-106

    14. Charrelation Matrix Based ICA

      • Alon Slapak, Arie Yeredor
      Pages 107-114

    15. Contrast Functions for Independent Subspace Analysis

      • Jason A. Palmer, Scott Makeig
      Pages 115-122

    16. Distributional Convergence of Subspace Estimates in FastICA: A Bootstrap Study

      • Jarkko Ylipaavalniemi, Nima Reyhani, Ricardo Vigário
      Pages 123-130

    17. New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem

      • Sylvain Le Corff, Gersende Fort, Eric Moulines
      Pages 131-138

    18. The Role of Whitening for Separation of Synchronous Sources

      • Miguel Almeida, Ricardo Vigário, José Bioucas-Dias
      Pages 139-146

    19. Simultaneous Diagonalization of Skew-Symmetric Matrices in the Symplectic Group

      • Frank C. Meinecke
      Pages 147-154
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  1. Latent Variable Analysis and Signal Separation

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About this book

This book constitutes the proceedings of the 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012, held in Tel Aviv, Israel, in March 2012. The 20 revised full papers presented together with 42 revised poster papers, 1 keynote lecture, and 2 overview papers for the regular, as well as for the special session were carefully reviewed and selected from numerous submissions. Topics addressed are ranging from theoretical issues such as causality analysis and measures, through novel methods for employing the well-established concepts of sparsity and non-negativity for matrix and tensor factorization, down to a variety of related applications ranging from audio and biomedical signals to precipitation analysis.

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