International Business Machines Corporation
AUTOMATIC MULTILABEL CLASSIFICATION USING MACHINE LEARNING

Abstract:

A computer-implemented method for automatic multilabel classification includes receiving a label matrix Y for multiple training instances. The label matrix Y includes multiple labels, each label representing a respective category. The method further includes computing an intermediate matrix YY.sup.T, where Y.sup.T is a transpose of the label matrix Y. The method further includes computing a basis matrix H by a non-negative matrix factorization of the intermediate matrix YY.sup.T. The method further includes generating a group testing matrix A by sampling the basis matrix H. The method further includes generating, for each training instance from the training instances, a reduced label vector z by computing a product of the group testing matrix A and a label vector y for respective training instance from the label matrix Y. The method further includes predicting multiple labels associated with an input based on the reduced label vector z.