  In this paper we offer a method and algorithm, which make possible fully
autonomous (unsupervised) detection of new classes, and learning following a
very parsimonious training priming (few labeled data samples only). Moreover,
new unknown classes may appear at a later stage and the proposed xClass method
and algorithm are able to successfully discover this and learn from the data
autonomously. Furthermore, the features (inputs to the classifier) are
automatically sub-selected by the algorithm based on the accumulated data
density per feature per class. As a result, a highly efficient, lean,
human-understandable, autonomously self-learning model (which only needs an
extremely parsimonious priming) emerges from the data. To validate our proposal
we tested it on two challenging problems, including imbalanced Caltech-101 data
set and iRoads dataset. Not only we achieved higher precision, but, more
significantly, we only used a single class beforehand, while other methods used
all the available classes) and we generated interpretable models with smaller
number of features used, through extremely weak and weak supervision.
