ML Anthology

Nonnegative Tucker Decomposition

Yong-Deok Kim, Seungjin Choi
CVPR 2007
doi:10.1109/CVPR.2007.383405 /cvpr/2007/kim2007cvpr-nonnegative/

Abstract

Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). The main contributions of this paper include: (1) multiplicative updating algorithms for NTD; (2) an initialization method for speeding up convergence; (3) a sparseness control method in tensor factorization. Through several computer vision examples, we show the useful behavior of the NTD, over existing NTF and NMF methods.

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Cite

Text

Kim and Choi. "Nonnegative Tucker Decomposition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383405

Markdown

[Kim and Choi. "Nonnegative Tucker Decomposition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/kim2007cvpr-nonnegative/) doi:10.1109/CVPR.2007.383405

BibTeX

@inproceedings{kim2007cvpr-nonnegative,
  title     = {{Nonnegative Tucker Decomposition}},
  author    = {Kim, Yong-Deok and Choi, Seungjin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383405},
  url       = {https://mlanthology.org/cvpr/2007/kim2007cvpr-nonnegative/}
}