Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
 
 
DownloadE-Mail
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem

Kim, D., Sra, S., & Dhillon, I. (2007). Fast Newton-type Methods for the Least Squares Nonnegative Matrix Approximation Problem. In C. Apte, D. Skillicorn, B. Liu, & S. Parthasarathy (Eds.), 2007 SIAM International Conference on Data Mining (pp. 343-354). Pittsburgh, PA, USA: Society for Industrial and Applied Mathematics.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CE1F-F Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-E326-2
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
https://epubs.siam.org/doi/10.1137/1.9781611972771.31 (Verlagsversion)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Kim, D, Autor
Sra, S1, Autor           
Dhillon, I, Autor
Affiliations:
1External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to be useful for a wide variety of applications ranging from document analysis and image processing to bioinformatics. There exist a few algorithms for nonnegative matrix approximation (NNMA), for example, Lee & Seung's multiplicative updates, alternating least squares, and certain gradient descent based procedures. All of these procedures suffer from either slow convergence, numerical instabilities, or at worst, theoretical un-soundness. In this paper we present new and improved algorithms for the least-squares NNMA problem, which are not only theoretically well-founded, but also overcome many of the deficiencies of other methods. In particular, we use non-diagonal gradient scaling to obtain rapid convergence. Our methods provide numerical results superior to both Lee & Seung's method as well to the alternating least squares (ALS) heuristic, which is known to work well in some situations but has no theoretical guarantees (Berry et al. 2006). Our approach extends naturally to include regularization and box-constraints, without sacrificing convergence guarantees. We present experimental results on both synthetic and real-world datasets to demonstrate the superiority of our methods, in terms of better approximations as well as efficiency.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2007-04
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 5219
DOI: 10.1137/1.9781611972771.31
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: Seventh SIAM International Conference on Data Mining (SDM 2007)
Veranstaltungsort: Minneapolis, MN, USA
Start-/Enddatum: 2007-04-26 - 2007-04-28

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: 2007 SIAM International Conference on Data Mining
Genre der Quelle: Konferenzband
 Urheber:
Apte, C, Herausgeber
Skillicorn, D, Herausgeber
Liu, B, Herausgeber
Parthasarathy, S, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Pittsburgh, PA, USA : Society for Industrial and Applied Mathematics
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 343 - 354 Identifikator: ISBN: 978-0-898716-30-6