Go to content
UR Home

Sparse Component Analysis: a New Tool for Data Mining

Georgiev, P., Pardalos, P., Theis, Fabian J., 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.

Full text not available from this repository.


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), ...


Export bibliographical data

Item type:Book section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Related URLs:
Keywords:Sparse Component Analysis - Blind Signal Separation - clustering
Dewey Decimal Classification:500 Science > 570 Life sciences
Created at the University of Regensburg:Unknown
Item ID:17315
Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de

Dissertations: dissertationen@ur.de

Research data: daten@ur.de

Contact persons