2019
DOI: 10.1109/access.2018.2890723
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Robust Hierarchical Learning for Non-Negative Matrix Factorization With Outliers

Abstract: Desirable properties of extensions of non-negative matrix factorization (NMF) include robustness in the presence of noises and outliers, ease of implementation, the guarantee of convergence, operation in an automatic fashion that trades off the balance between data approximation and model simplicity well, and the capability to model the inherently sequential structure of time-series signals. The state-of-the-art methods typically have only a subset of these aforementioned properties and seldom simultaneously p… Show more

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