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.

Full text not available from this repository.

## 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), ...

## Export bibliographical data

Item Type: | Book Section | ||||
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Date: | 2007 | ||||

Institutions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang | ||||

Related URLs: |
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Keywords: | Sparse Component Analysis - Blind Signal Separation - clustering | ||||

Dewey Decimal Classification: | 500 Science > 570 Life sciences | ||||

Status: | Published | ||||

Refereed: | Unknown | ||||

Created at the University of Regensburg: | Unknown | ||||

Deposited On: | 15 Oct 2010 08:42 | ||||

Last Modified: | 15 Oct 2010 08:42 | ||||

Item ID: | 17315 |