DOE OSTI · 3002147
On Rank Selection for Nonnegative Matrix Factorization
Abstract
Rank selection, i.e. the choice of factorization rank, is the first step in constructing Nonnegative Matrix Factorization (NMF) models. It is a long-standing problem which is not unique to NMF, but arises in most models which attempt to decompose data into its underlying components. Since these models are often used in the unsupervised setting, the rank selection problem is further complicated by the lack of ground truth labels. In this paper, we review and empirically evaluate the most commonly used schemes for NMF rank selection.
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Eswar, Srinivas [Argonne National Laboratory], Hayashi, Koby [Pacific Northwest National Laboratory (PNNL)], Cobb, Benjamin [Georgia Institute of Technology], Kannan, Ramakrishnan {ramki} [ORNL] (ORCID:0000000258524806), Ballard, Grey [Wake Forest University, Winston-Salem], Vuduc, Richard [Georgia Institute of Technology, Atlanta], Park, Haesun [Georgia Institute of Technology, Atlanta]. 2024-12-01. On Rank Selection for Nonnegative Matrix Factorization. https://doi.org/10.1109/bigdata62323.2024.10825324
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