Extracting experimental information from large matrixes. 1. A new algorithm for the application of matrix rank analysis

G. Peintler, I. Nagypál, A. Jancsó, Irving R. Epstein, Kenneth Kustin

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

For many, especially complex, systems, modern spectroscopic measurements can be generated as large experimental data sets in matrix form. We report a new algorithm for the application of matrix rank analysis to extract significant experimental information from these large matrixes. The algorithm may be used to detect and remove erroneous rows and/or columns from the matrixes and to monitor the most significant experimental information along the rows and/or columns of the data sets. A new method for determining the number of absorbing species and a new concept for the treatment of experimental errors are presented. The algorithm is illustrated on real experimental examples.

Original languageEnglish
Pages (from-to)8013-8020
Number of pages8
JournalJournal of Physical Chemistry A
Volume101
Issue number43
Publication statusPublished - Oct 23 1997

Fingerprint

matrices
complex systems
Large scale systems

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry

Cite this

Extracting experimental information from large matrixes. 1. A new algorithm for the application of matrix rank analysis. / Peintler, G.; Nagypál, I.; Jancsó, A.; Epstein, Irving R.; Kustin, Kenneth.

In: Journal of Physical Chemistry A, Vol. 101, No. 43, 23.10.1997, p. 8013-8020.

Research output: Contribution to journalArticle

@article{89ccb0ae0e1a45a886e602315b07a5da,
title = "Extracting experimental information from large matrixes. 1. A new algorithm for the application of matrix rank analysis",
abstract = "For many, especially complex, systems, modern spectroscopic measurements can be generated as large experimental data sets in matrix form. We report a new algorithm for the application of matrix rank analysis to extract significant experimental information from these large matrixes. The algorithm may be used to detect and remove erroneous rows and/or columns from the matrixes and to monitor the most significant experimental information along the rows and/or columns of the data sets. A new method for determining the number of absorbing species and a new concept for the treatment of experimental errors are presented. The algorithm is illustrated on real experimental examples.",
author = "G. Peintler and I. Nagyp{\'a}l and A. Jancs{\'o} and Epstein, {Irving R.} and Kenneth Kustin",
year = "1997",
month = "10",
day = "23",
language = "English",
volume = "101",
pages = "8013--8020",
journal = "Journal of Physical Chemistry A",
issn = "1089-5639",
publisher = "American Chemical Society",
number = "43",

}

TY - JOUR

T1 - Extracting experimental information from large matrixes. 1. A new algorithm for the application of matrix rank analysis

AU - Peintler, G.

AU - Nagypál, I.

AU - Jancsó, A.

AU - Epstein, Irving R.

AU - Kustin, Kenneth

PY - 1997/10/23

Y1 - 1997/10/23

N2 - For many, especially complex, systems, modern spectroscopic measurements can be generated as large experimental data sets in matrix form. We report a new algorithm for the application of matrix rank analysis to extract significant experimental information from these large matrixes. The algorithm may be used to detect and remove erroneous rows and/or columns from the matrixes and to monitor the most significant experimental information along the rows and/or columns of the data sets. A new method for determining the number of absorbing species and a new concept for the treatment of experimental errors are presented. The algorithm is illustrated on real experimental examples.

AB - For many, especially complex, systems, modern spectroscopic measurements can be generated as large experimental data sets in matrix form. We report a new algorithm for the application of matrix rank analysis to extract significant experimental information from these large matrixes. The algorithm may be used to detect and remove erroneous rows and/or columns from the matrixes and to monitor the most significant experimental information along the rows and/or columns of the data sets. A new method for determining the number of absorbing species and a new concept for the treatment of experimental errors are presented. The algorithm is illustrated on real experimental examples.

UR - http://www.scopus.com/inward/record.url?scp=0031248855&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031248855&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0031248855

VL - 101

SP - 8013

EP - 8020

JO - Journal of Physical Chemistry A

JF - Journal of Physical Chemistry A

SN - 1089-5639

IS - 43

ER -