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Sparse Component Analysis: a New Tool for Data Mining

Georgiev, P. and Pardalos, P. and Theis, Fabian J. and Cichocki, A. and Bakardjian, H. (2007) Sparse Component Analysis: a New Tool for Data Mining. In: Pardalos, P. and Boginski, V. and Vazacopoulos, A., (eds.) Data mining in biomedicine. Optimization and its applications, 7, Part I. Springer, New York, pp. 91-116. ISBN 0-387-69318-1, 978-0-387-69318-7.

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Abstract

In many practical problems for data mining the data X under consideration (given as (m × N)-matrix) is of the form X = AS, where the matrices A and S with dimensions m×n and n × N respectively (often called mixing matrix or dictionary and source matrix) are unknown (m ≤ n < N). We formulate conditions (SCA-conditions) under which we can recover A and S uniquely (up to scaling and permutation), ...

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Export bibliographical data

Item Type:Book Section
Date:2007
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Related URLs:
URLURL Type
http://springerlink.com/content/k5k1l2m7365u5w06/Publisher
Keywords:Sparse Component Analysis - Blind Signal Separation - clustering
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:15 Oct 2010 08:42
Last Modified:15 Oct 2010 08:42
Item ID:17315
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