Introduction to Nonnegative Matrix Factorization
arXiv:1703.00663
Abstract
In this paper, we introduce and provide a short overview of nonnegative matrix factorization (NMF). Several aspects of NMF are discussed, namely, the application in hyperspectral imaging, geometry and uniqueness of NMF solutions, complexity, algorithms, and its link with extended formulations of polyhedra. In order to put NMF into perspective, the more general problem class of constrained low-rank matrix approximation problems is first briefly introduced.
18 pages, 4 figures
References in corpus (9)
- Generalized power method for sparse principal component analysis
- Global Optimality of Local Search for Low Rank Matrix Recovery
- Matrix Completion has No Spurious Local Minimum
- Non-convex Robust PCA
- Matrix Completion from a Few Entries
- The Non-convex Geometry of Low-rank Matrix Optimization
- A Fast Gradient Method for Nonnegative Sparse Regression with Self Dictionary
- Low Rank Approximation with Entrywise -Norm Error
- Nonnegative rank depends on the field II
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