Low-Rank Matrix Approximation Task
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A Low-Rank Matrix Approximation Task is a matrix decomposition task that is an approximation task.
- Example(s):
- See: Exact Matrix Compression.
References
2013
- (Lee et al., 2013) ⇒ Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. (2013). “Local Low-rank Matrix Approximation.” In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28.
- QUOTE: Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. ...
2011
- (Mahoney, 2011) ⇒ Michael W. Mahoney. (2011). “Randomized Algorithms for Matrices and Data.” Now Publishers Inc.. ISBN:1601985061, 9781601985064
- QUOTE: Randomized Algorithms for Matrices and Data provides a detailed overview, appropriate for both students and researchers from all of these areas, of recent work on the theory of randomized matrix algorithms as well as the application of those ideas to the solution of practical problems in large-scale data analysis. By focusing on ubiquitous and fundamental problems such as least squares approximation and low-rank matrix approximation that have been at the center of recent developments, an emphasis is placed on a few simple core ideas that underlie not only recent theoretical advances but also the usefulness of these algorithmic tools in large-scale data applications.