A Canonical Correlation Analysis Based Method for Improving BSS of Two Related Data Sets (2012)

AUTHORS:

ABSTRACT:

We consider an extension of ICA and BSS for separating mutually dependent and independent components from two related data sets. We propose a new method which first uses canonical correlation analysis for detecting subspaces of independent and dependent components. Different ICA and BSS methods can after this be used for final separation of these components. Our method has a sound theoretical basis, and it is straightforward to implement and computationally not demanding. Experimental results on synthetic and real-world fMRI data sets demonstrate its good performance.

URL:
http://dx.doi.org/10.1007/978-3-642-28551-6\_12

@inproceedings{
    JKarhunen-2012-ICA_whyj,
    author = "Karhunen, Juha and Hao, Tele and Ylipaavalniemi, Jarkko",
    responsibleauthor = "Karhunen, Juha and Hao, Tele and Ylipaavalniemi, Jarkko",
    eventtime = "March 12-15",
    doi = "10.1007/978-3-642-28551-6\_12",
    language = "eng",
    title = "{A Canonical Correlation Analysis Based Method for Improving BSS of Two Related Data Sets}",
    eventlocation = "Tel Aviv, Israel",
    country = "Germany",
    abstract = "We consider an extension of ICA and BSS for separating mutually dependent and independent components from two related data sets. We propose a new method which first uses canonical correlation analysis for detecting subspaces of independent and dependent components. Different ICA and BSS methods can after this be used for final separation of these components. Our method has a sound theoretical basis, and it is straightforward to implement and computationally not demanding. Experimental results on synthetic and real-world fMRI data sets demonstrate its good performance.",
    issn = "0302-9743",
    month = "March",
    juforank = "NA",
    url = "http://dx.doi.org/10.1007/978-3-642-28551-6\_12",
    flags = "copy",
    il = "no",
    year = "2012",
    keywords = "independent component analysis, canonical component analysis",
    unitcode = "T306-100",
    kay = "NA",
    impactfactor = "A4",
    booktitle = "10th International Conference on Latent Variable Analysis and Source Separation (LVA/ICA 2012)",
    pages = "91--98"
}

AUTHORS:

ABSTRACT:

URL: http://dx.doi.org/10.1007/978-3-642-28551-6\_12

URL:
http://dx.doi.org/10.1007/978-3-642-28551-6\_12