Slow features nonnegative matrix factorization for temporal data decomposition

Lazaros Zafeiriou; Symeon Nikitidis; Stefanos Zafeiriou; Maja Pantic

doi:10.1109/ICIP.2014.7025286

Slow features nonnegative matrix factorization for temporal data decomposition

Lazaros Zafeiriou, Symeon Nikitidis, Stefanos Zafeiriou, Maja Pantic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

48 Downloads (Pure)

Abstract

In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database.

Original language	Undefined
Title of host publication	Proceedings of IEEE International Conference on Image Processing (ICIP 2014)
Place of Publication	USA
Publisher	IEEE Computer Society
Pages	1430-1434
Number of pages	5
ISBN (Print)	978-1-4799-5751-4
DOIs	https://doi.org/10.1109/ICIP.2014.7025286
Publication status	Published - Oct 2014
Event	IEEE International Conference on Image Processing, ICIP 2014 - Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 https://icip2014.wp.imt.fr/

Publication series

Name
Publisher	IEEE Computer Society

Conference

Conference	IEEE International Conference on Image Processing, ICIP 2014
Abbreviated title	ICIP
Country/Territory	France
City	Paris
Period	27/10/14 → 30/10/14
Internet address	https://icip2014.wp.imt.fr/

Keywords

HMI-HF: Human Factors
EWI-25823
EC Grant Agreement nr.: FP7/2007-2013
EC Grant Agreement nr.: FP7/288235
IR-95230
Nonnegative Matrix Factorization
Slow Features Analysis
METIS-309949
Facial behaviour dynamics analysis

Access to Document

10.1109/ICIP.2014.7025286

Pantic_Slow_Features_Nonnegative_Matrix_Factorization

Cite this

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title = "Slow features nonnegative matrix factorization for temporal data decomposition",

abstract = "In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database.",

keywords = "HMI-HF: Human Factors, EWI-25823, EC Grant Agreement nr.: FP7/2007-2013, EC Grant Agreement nr.: FP7/288235, IR-95230, Nonnegative Matrix Factorization, Slow Features Analysis, METIS-309949, Facial behaviour dynamics analysis",

author = "Lazaros Zafeiriou and Symeon Nikitidis and Stefanos Zafeiriou and Maja Pantic",

note = "eemcs-eprint-25823 ; null ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

month = oct,

doi = "10.1109/ICIP.2014.7025286",

language = "Undefined",

isbn = "978-1-4799-5751-4",

publisher = "IEEE Computer Society",

pages = "1430--1434",

booktitle = "Proceedings of IEEE International Conference on Image Processing (ICIP 2014)",

address = "United States",

url = "https://icip2014.wp.imt.fr/",

}

Zafeiriou, L, Nikitidis, S, Zafeiriou, S & Pantic, M 2014, Slow features nonnegative matrix factorization for temporal data decomposition. in Proceedings of IEEE International Conference on Image Processing (ICIP 2014). IEEE Computer Society, USA, pp. 1430-1434, IEEE International Conference on Image Processing, ICIP 2014, Paris, France, 27/10/14. https://doi.org/10.1109/ICIP.2014.7025286

Slow features nonnegative matrix factorization for temporal data decomposition. / Zafeiriou, Lazaros; Nikitidis, Symeon; Zafeiriou, Stefanos et al.

Proceedings of IEEE International Conference on Image Processing (ICIP 2014). USA : IEEE Computer Society, 2014. p. 1430-1434.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Pantic, Maja

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AB - In this paper, we combine the principles of temporal slowness and nonnegative parts-based learning into a single framework that aims to learn slow varying parts-based representations of time varying sequences. We demonstrate that the proposed algorithm arises naturally by embedding the Slow Features Analysis trace optimization problem in the nonnegative subspace learning framework and derive novel multiplicative update rules for its optimization. The usefulness of the developed algorithm is demonstrated for unsupervised facial behaviour dynamics analysis on MMI database.

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KW - EC Grant Agreement nr.: FP7/2007-2013

KW - EC Grant Agreement nr.: FP7/288235

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KW - Slow Features Analysis

KW - METIS-309949

KW - Facial behaviour dynamics analysis

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