Low Rank Approximation - Algorithms, Implementation, Applications
By Ivan Markovsky. Communications and Control Engineering, 2011
336 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/082f5aa6341f2eee0592d93d8a30cdbc2e94b9d0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- P. Seiler
- Jennifer Annoni
- Florian Dörfler
- Stephen P. Boyd
- Corinne Horn
- Madeleine Udell
- Reza Bosagh Zadeh
- John Lygeros
In Computational Mechanics
- P. Seiler
- Jennifer Annoni
- Reza Bosagh Zadeh
- Madeleine Udell
- Corinne Horn
- Florian Dörfler
- Stephen P. Boyd
- Hui Fan
Citations in the corpus
- An Introduction to Neural Information Retrieval (2018, neutral): “, 1990] involves performing singular value decomposition (SVD) [Golub and Reinsch, 1970] on a term-document (or term-passage) matrix X to obtain its low-rank approximation [Markovsky, 2011].”
- Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, background): “data matrix resulting in higher algorithmic and computational complexity [47].”
- Brief review of image denoising techniques (2019, background): “By exploiting the low-rank prior of the matrix, this model can effectively reduce the noise in an image [67, 68].”
- Generalized Low Rank Models (2014, methodology): “atrix factorization in a 2009 paper on penalized matrix decomposition, with applications to sparse PCA and canonical correlation analysis [WTH09]. Recently, Markovsky’s book on low rank approximation [Mar12] reviewed some of this literature, with a focus on applications in system, control, and signa”
- Behavioral systems theory in data-driven analysis, signal processing, and control (2021, background): “…Markovsky), dorfler@ethz.ch (Florian Dörfler) the global total least squares (Roorda and Heij, 1995), deterministic subspace (Van Overschee and De Moor, 1996), and structured low-rank approximation (Markovsky, 2013, 2019) approaches to system identification are motivated by (Willems, 1986, 1987).”
- A method to construct reduced‐order parameter‐varying models (2017, methodology): “Proof This is a consequence of the Eckart-Young-Mirsky Theorem [28, 29].”