Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Machine Learning

arXiv:2003.03506 (cs)
[Submitted on 7 Mar 2020]

Title:Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization

Authors:Thirunavukarasu Balasubramaniam, Richi Nayak, Chau Yuen
View PDF
Abstract:Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors.
Comments: To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE)
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2003.03506 [cs.LG]
  (or arXiv:2003.03506v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2003.03506
Related DOI: https://doi.org/10.1109/TKDE.2020.2967045

Submission history

From: Thirunavukarasu Balasubramaniam [view email]
[v1] Sat, 7 Mar 2020 03:34:53 UTC (5,097 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2020-03
Change to browse by:
cs
stat
stat.ML

References & Citations

DBLP - CS Bibliography

listing | bibtex
Balasubramaniam Thirunavukarasu
Richi Nayak
Chau Yuen
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)