| 摘要 |
Methods and systems for clustering information items using nonnegative tensor factorization are disclosed. A processing device receives one or more class labels, each corresponding to an information item, a selection for a nonnegative tensor factorization model having an associated objective function and one or more parameter values, each corresponding to one of one or more penalty constraints. The processing device determines a constrained objective function based on the objective function associated with the selected nonnegative tensor factorization model, the one or more parameter values and the one or more class labels and including the one or more penalty constraints. The processing device determines clusters for the plurality of information items by evaluating the constrained objective function. Pairwise constraints may be received in addition to or instead of the class labels.
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