Learning from Point Sets with Observational Bias

Abstract

Many objects can be represented as sets of multi-dimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown un-derlying distribution, and then estimate the sim-ilarities between these distributions. In realistic situations, however, the point sets are often sub-ject to sampling biases due to variable or incon-sistent observation actions. These biases can fun-damentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional diver-gences to correct these distortions and learn from biased point sets effectively. Our empirical study shows that the proposed method can successfully correct the biases and achieve satisfactory learn-ing performance. 1