Double truncated nuclear norm-based matrix decomposition with application to background modeling

Abstract Many topics in pattern recognition and machine learning, such as subspace learning, image restoration, background modeling, can be viewed as the matrix decomposing problem. Double nuclear norm-based matrix decomposition (DNMD) is a new emerging method for dealing with the image data corrupt...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Yang, Zhangjing [verfasserIn]

Zhang, Hui

Xu, Danhua

Zhang, Fanlong

Yang, Guowei

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2018

Schlagwörter:

Truncated nuclear norm

Low rank

Principal component analysis

Matrix decomposition

Anmerkung:

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Übergeordnetes Werk:

Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 14(2018), 11 vom: 01. Dez., Seite 14921-14930

Übergeordnetes Werk:

volume:14 ; year:2018 ; number:11 ; day:01 ; month:12 ; pages:14921-14930

Links:

Volltext

DOI / URN:

10.1007/s12652-018-1148-x

Katalog-ID:

SPR054396174

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