Techniques for Generating Balanced and Class-Independent Training Data From Unlabeled Data Set

An apparatus for generating training data from an unlabeled data set, the apparatus comprising:

• a memory;
• and
• at least one processor device, coupled to the memory, operative to:
• select a small initial set of data from the unlabeled data set;
• acquire labels for the initial set of data selected from the unlabeled data set resulting in labeled data;
• cluster the data in the unlabeled data set using a semi-supervised clustering process along with the labeled data to produce a plurality of data clusters;
• choose data samples from each of the clusters to use as the training data;
• and
• repeat the selecting, presenting, clustering and choosing steps with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.

An article of manufacture for generating training data from an unlabeled data set, comprising a machine-readable recordable medium containing one or more programs which when executed implement the steps of:

• selecting a small initial set of data from the unlabeled data set;
• acquiring labels for the initial set of data selected from the unlabeled data set resulting in labeled data;
• clustering the data in the unlabeled data set using a semi-supervised clustering process along with the labeled data to produce a plurality of data clusters;
• choosing data samples from each of the clusters to use as the training data;
• and
• repeating the selecting, presenting, clustering and choosing steps with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.