ORTHOGONAL NONNEGATIVE TUCKER DECOMPOSITION

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Junjun PAN
  • Michael K. NG
  • Ye LIU
  • Xiongjun ZHANG
  • Hong YAN

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)B55-B81
Journal / PublicationSIAM Journal on Scientific Computing
Volume43
Issue number1
Online published7 Jan 2021
Publication statusPublished - 2021

Abstract

In this paper, we study nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, and hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.

Research Area(s)

  • Image processing, Nonnegative tensor, Tucker decomposition

Citation Format(s)

ORTHOGONAL NONNEGATIVE TUCKER DECOMPOSITION. / PAN, Junjun; NG, Michael K.; LIU, Ye; ZHANG, Xiongjun; YAN, Hong.

In: SIAM Journal on Scientific Computing, Vol. 43, No. 1, 2021, p. B55-B81.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review