Subtracting a best rank-1 approximation may increase tensor rank

By Alwin Stegeman, Pierre Comon. 2009

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Computational Mechanics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Anima Anandkumar
2. Rong Ge
3. Sham M. Kakade
4. Daniel J. Hsu
5. Yuanzhi Li
6. Matus Telgarsky
7. Tengyu Ma
8. Michael I. Jordan

In Computational Mathematics

1. Anima Anandkumar
2. Rong Ge
3. Daniel J. Hsu
4. Sham M. Kakade
5. Matus Telgarsky
6. Yuanzhi Li
7. Zehao Dou
8. Alessandro Lazaric

In Computational Mechanics

1. Rong Ge
2. Sham M. Kakade
3. Anima Anandkumar
4. Daniel J. Hsu
5. Matus Telgarsky
6. Zeyuan Allen-Zhu
7. Jason D. Lee
8. Prateek Jain

Citations in the corpus

• A literature survey of low‐rank tensor approximation techniques (2013, neutral): “Of course, such a greedy approach will not yield the best rank-R approximation after R steps [241].”
• Tensor decompositions for learning latent variable models (2012, background): “In addition, removal of the best rank-1 approximation of a (general) tensor may increase the tensor rank of the residual (Stegeman and Comon, 2010).”