Assessing methods for evaluating the number of components in non-negative matrix factorization

José M. Maisog, Andrew T. Demarco, Karthik Devarajan, Stanley Young, Paul Fogel, George Luta

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m × n data matrix X into an m × k matrix W and a k × n matrix H, so that X ≈ W × H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components into which X has been decomposed. The question arises: how does one choose k? In this paper, we first assess methods for estimating k in the context of NMF in synthetic data. Second, we examine the effect of normalization on this estimate’s accuracy in empirical data. In synthetic data with orthogonal underlying components, methods based on PCA and Brunet’s Cophenetic Correlation Coefficient achieved the highest accuracy. When evaluated on a well-known real dataset, normalization had an unpredictable effect on the estimate. For any given normalization method, the methods for estimating k gave widely varying results. We conclude that when estimating k, it is best not to apply normalization. If the underlying components are known to be orthogonal, then Velicer’s MAP or Minka’s Laplace-PCA method might be best. However, when the orthogonality of the underlying components is unknown, none of the methods seemed preferable.

Original languageEnglish
Article number2840
Issue number22
StatePublished - Nov 1 2021


  • Factorization rank
  • High-dimensional data
  • Non-negative matrix factorization
  • Normalization
  • Number of factored components
  • PCA
  • Unsupervised learning


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