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- W2139115028 abstract "An exact nonnegative matrix decomposition algorithm is proposed. This is achieved by 1) Taking a nonlinear approximation of a sparse real-valued dataset at a given tolerance-to-error constraint, c; Choosing an arbitrary lectic ordering on the rows or column entries; And, then systematically applying a closure operator, so that all closures are selected. Assuming a nonnegative hierarchical closure structure (a Galois lattice) ensures the data has a unique ordered overcomplete dictionary representation. Parts-based constraints on these closures can then be used to specify and supervise the form of the solution. We illustrate that this approach outperforms NMF on two standard NMF datasets: it exhibits the properties described above; It is correct and exact. (8 pages)" @default.
- W2139115028 created "2016-06-24" @default.
- W2139115028 creator A5022040369 @default.
- W2139115028 date "2013-01-01" @default.
- W2139115028 modified "2023-09-25" @default.
- W2139115028 title "Ghostbusters: A Parts-based NMF algorithm" @default.
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- W2139115028 doi "https://doi.org/10.1049/ic.2013.0050" @default.
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