ORTHOGONAL NONNEGATIVE TUCKER DECOMPOSITION
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | B55-B81 |
| Journal / Publication | SIAM Journal on Scientific Computing |
| Volume | 43 |
| Issue number | 1 |
| Online published | 7 Jan 2021 |
| Publication status | Published - 2021 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85102809410&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(69655a0e-1eac-4c51-87bc-814abee6931c).html |
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 et al.
In: SIAM Journal on Scientific Computing, Vol. 43, No. 1, 2021, p. B55-B81.
In: SIAM Journal on Scientific Computing, Vol. 43, No. 1, 2021, p. B55-B81.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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