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Sparse coding is a representation learning method which aims at finding a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms and they compose a dictionary. Atoms in the dictionary are not required to be orthogonal, and they may be an over-complete spanning set. This problem setup also allows the dimensionality of the signals being represented to be higher than the one of the signals being observed. The above two properties lead to having seemingly redundant atoms that allow multiple representations of the same signal but also provide an improvement in sparsity and flexibility of the representation.
References
- ↑ Pipatsrisawat, Knot; Palyan, Akop; Chavira, Mark; Choi, Arthur; Darwiche, Adnan (2008). "Solving Weighted Max-SAT Problems in a Reduced Search Space: A Performance Analysis". Journal on Satisfiability Boolean Modeling and Computation (JSAT). UCLA. 4(2008): 4. Retrieved 18 April 2022.
- 1 2 Vanderbei (1991)
- ↑ Alvarado (1997)
- ↑ Adlers & Björck (2000) Reprinted as Appendix A, in Mikael Adlers, 2000, Topics in Sparse Least Squares Problems, Linkoping Studies in Science and Technology", Linkoping University, Sweden.