Learning Multiclassifiers with Predictive Features that Vary with Data Distribution

In many real-world big data applications, the data distribution is not homogeneous over entire data, but instead varies across groups/clusters of data samples. Although a model's predictive performance remains vital, there is also a need to learn succinct sets of features that evolve and capture smooth variations in data distribution. These small sets of features not only lead to high prediction accuracy, but also discover the important...