A new Jacobi-like method for joint diagonalization of arbitrary non-defective matrices

By Redha Iferroudjene, Karim Abed-Meraim, Adel Belouchrani. 2009

49 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Vision and Pattern Recognition, Optimization & Training, Signal Processing

How researchers see this paper

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

In Analytical Chemistry

1. Simon Lacoste-Julien
2. Anastasia Podosinnikova
3. Francis R. Bach
4. Arthur Gretton
5. Bernhard Schölkopf
6. Kenji Fukumizu
7. Michael I. Jordan
8. Alex Smola

In Computer Vision and Pattern Recognition

1. Simon Lacoste-Julien
2. Francis R. Bach
3. Anastasia Podosinnikova
4. Luc Van Gool
5. Ivan Laptev
6. Cordelia Schmid
7. Andrew Zisserman
8. Li Fei-Fei

In Optimization & Training

1. Simon Lacoste-Julien
2. Francis R. Bach
3. Peter Richtárik
4. Nicolas Loizou
5. Anastasia Podosinnikova
6. Sebastian U. Stich
7. Zeyuan Allen-Zhu
8. Martin Jaggi

In Signal Processing

1. Simon Lacoste-Julien
2. Francis R. Bach
3. Anastasia Podosinnikova
4. Arthur Gretton
5. Bernhard Schölkopf
6. Sebastian U. Stich
7. Kenji Fukumizu
8. Julien Mairal

Citations in the corpus

• Beyond CCA: Moment Matching for Multi-View Models (2016, positive): “To solve the problem (31), we use the Jacobi-like nonorthogonal joint diagonalization (NOJD) algorithms (e.g., Fu & Gao, 2006; Iferroudjene et al., 2009; Luciani & Albera, 2010).”