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Discriminative Nonnegative Matrix Factorization for Dimensionality Reduction

Babaee, Mohammadreza and Tsoukalas, Stefanos and Babaee, Maryam and Rigoll, Gerald and Datcu, Mihai (2015) Discriminative Nonnegative Matrix Factorization for Dimensionality Reduction. Neurocomputing, 173 (Part 2), pp. 212-223. Elsevier. DOI: 10.1016/j.neucom.2014.12.124 ISSN 0925-2312

Full text not available from this repository.

Official URL: http://www.sciencedirect.com/science/journal/09252312

Abstract

Nonnegative Matrix Factorization (NMF) has been widely used for different purposes such as feature learning, dictionary leaning and dimensionality reduction in data mining and computer vision. In this work, we present a label constrained NMF, namely Discriminative Nonnegative Matrix Factorization (DNMF), which utilizes the label information of a fraction of the data as a discriminative constraint. The labeled samples are used in a regularization term, which is a linear regression based on the samples, coupled with the main objective function of NMF. In contrast to recently proposed semi-supervised NMF techniques, the proposed approach does not merge the samples with the same label into a single point. However, the algorithm enforces the samples with the same label to be aligned on the same axis in the new representation. The performed experiments on synthetic and real datasets expose the strength of our proposed method compared to the state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/100121/
Document Type:Article
Title:Discriminative Nonnegative Matrix Factorization for Dimensionality Reduction
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Babaee, MohammadrezaTechnical University Munich, GermanyUNSPECIFIED
Tsoukalas, StefanosTechnical University Munich, GermanyUNSPECIFIED
Babaee, MaryamUniversity of IsfahanUNSPECIFIED
Rigoll, GeraldTU MunichUNSPECIFIED
Datcu, MihaiDLRUNSPECIFIED
Date:2015
Journal or Publication Title:Neurocomputing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:173
DOI :10.1016/j.neucom.2014.12.124
Page Range:pp. 212-223
Editors:
EditorsEmail
Tom, Heskest.heskes@science.ru.nl
Publisher:Elsevier
ISSN:0925-2312
Status:Published
Keywords:Dimensionality reduction, nonnegative matrix factorization, data mining
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Schwarz, Gottfried
Deposited On:30 Nov 2015 10:38
Last Modified:06 Sep 2019 15:18

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