The paper derives a rank-selection rule for nonnegative matrix factorization from a common-cause predictability inequality and finds that the resulting features are stable across noise and random seeds on several image datasets.
Assessing methods for evaluating the number of components in non-negative matrix factorization,
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Nonnegative matrix factorization and the principle of the common cause
The paper derives a rank-selection rule for nonnegative matrix factorization from a common-cause predictability inequality and finds that the resulting features are stable across noise and random seeds on several image datasets.