Optimal Online Data Sampling or How to Hire the Best Secretaries

The problem of online sampling of data, can be seen as a generalization of the classical secretary problem. The goal is to maximize the probability of picking the k highest scoring samples in our data, making the decision to select or reject a sample online. We present a new and simple online algorithm to optimally make this selection. We then apply this algorithm to a sequence of images taken by a mobile robot, with the goal of identifying the most interesting and informative images.