The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Non-negative tensor factorization with applications to statistics and computer vision
Abstract
We derive algorithms for finding a non-negative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii) sparse image coding in computer vision, and (iii) model selection problems. We derive a "direct" positive-preserving gradient descent algorithm and an alternating scheme based on repeated multiple rank-1 problems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Chen, C., Zhao, L., Bian, J., Xing, C., & Liu, T.-Y. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330663 · Cited by 73
Online Non-Negative Convolutive Pattern Learning for Speech Signals
Wang, D., Vipperla, R., Evans, N., & Zheng, T. F. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2222381 · Cited by 25
Metadata
- Authors
- Shashua, A., & Hazan, T.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 792-799
- OpenAlex
- W2144920235
- Cited by
- 490
- References
- 1
- FWCI
- 4.00
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Non-negative tensor factorization with applications to statistics and computer vision. https://app.cochat.ai/papers/10.1145/1102351.1102451
- The paper
- Shashua, A., & Hazan, T. (2005). Non-negative tensor factorization with applications to statistics and computer vision. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102451
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
