All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Bayesian Blind Source Separation with Unknown Prior Covariance

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F15%3A00231473" target="_blank" >RIV/68407700:21340/15:00231473 - isvavai.cz</a>

  • Alternative codes found

    RIV/67985556:_____/15:00447092

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-22482-4_41" target="_blank" >http://dx.doi.org/10.1007/978-3-319-22482-4_41</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-22482-4_41" target="_blank" >10.1007/978-3-319-22482-4_41</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Bayesian Blind Source Separation with Unknown Prior Covariance

  • Original language description

    The task of blind source separation (BSS) is to recover original signal sources which are observed only via their superposition with unknown weights. Since we are interested in estimation of the number of relevant sources in noisy observation, we use theBayesian formulation which automatically removes spurious sources. A tool for this behavior is joint estimation of the unknown prior covariance matrix of the sources in tandem with the sources. In this work, we study the effect of various choices of thecovariance matrix structure. Specifically, we compare models using the automatic relevance determination (ARD) principle on the first and the second diagonal, as well as full covariance matrix with Wishart prior. We obtain five versions of the variational BSS algorithm. These are tested on synthetic data and on a selected dataset from dynamic renal scintigraphy. MATLAB implementation of the methods is available for download.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Latent Variable Analysis and Signal Separation

  • ISBN

    978-3-319-22481-7

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    352-359

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Liberec

  • Event date

    Aug 25, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article