B-spline smoothing of feature vectors in nonnegative matrix factorization

Rafał Zdunek, Andrzej Cichocki, Tatsuya Yokota

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Nonnegative Matrix Factorization (NMF) captures nonnegative, sparse and parts-based feature vectors from the set of observed nonnegative vectors. In many applications, the features are also expected to be locally smooth. To incorporate the information on the local smoothness to the optimization process, we assume that the features vectors are conical combinations of higher degree B-splines with a given number of knots. Due to this approach the computational complexity of the optimization process does not increase considerably with respect to the standard NMF model. The numerical experiments, which were carried out for the blind spectral unmixing problem, demonstrate the robustness of the proposed method.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 13th International Conference, ICAISC 2014, Proceedings
PublisherSpringer Verlag
Pages72-81
Number of pages10
EditionPART 2
ISBN (Print)9783319071756
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014 - Zakopane, Poland
Duration: 1 Jun 20145 Jun 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8468 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2014
Country/TerritoryPoland
CityZakopane
Period1/06/145/06/14

Keywords

  • B-Splines
  • Feature Extraction
  • NMF
  • Spectral Unmixing

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