  A common problem in machine learning is to deal with datasets with disjoint
label spaces and missing labels. In this work, we introduce the openXDATA tool
that completes the missing labels in partially labelled or unlabelled datasets
in order to generate multi-target data with labels in the joint label space of
the datasets. To this end, we designed and implemented the cross-data label
completion (CDLC) algorithm that uses a multi-task shared-hidden-layer DNN to
iteratively complete the sparse label matrix of the instances from the
different datasets. We apply the new tool to estimate labels across four
emotion datasets: one labeled with discrete emotion categories (e.g., happy,
sad, angry), one labeled with continuous values along arousal and valence
dimensions, one with both kinds of labels, and one unlabeled. Testing with
drop-out of true labels, we show the ability to estimate both categories and
continuous labels for all of the datasets, at rates that approached the ground
truth values. openXDATA is available under the GNU General Public License from
https://github.com/fweninger/openXDATA.
