Given a classification task:
Training dataset "A" with labelled data of 10 classes.
Training dataset "B" with unlabelled data of 11 classes. Compared to "A", "B"contains one extra class, we can call it unknown class. Its size is also unknown. It represents the fact that there are certain "things" do exist in the world but it is not observed in our labelled training set.
Test dataset "C" contain 11 classes same as "B"
Any suggestion on how to handle this extra class during classification and predict "C" correctly?
Just want you know that this problem is related to the NIST iVector Challenge, the language identification task