Interpoint Similarity-Based Uncertainty Measure for Robust Learning

Identifying reliable information from the information ocean is a natural talent of human being, which can be hardly formalized by machines. In this paper, we show how to measure the degree of uncertainty in terms of interpoint similarity. For applications under complex uncertainty, it is desirable that we provide a systematic way for users to identify reliable information. In our approach, the similarity uncertainty for different kinds of data sets is defined according to the Shannon entropy theory. Then, similarity constrained models are designed to guarantee superior learning performance under uncertainty. Experiments using both simulation data set and several public data sets, can clearly demonstrate significant improvements of the proposed method under large uncertainty.