We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapo lation problem also has many practical application, e. g. for training classifiers that have to op erate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding proba bility distributions into a reproducing kernel Hilbert space, and learning op erators by vector-valued regression. We illustrate the working principles and the practical usefulness of our method by experiments on synthetic and real data. We also highlight an exemplary application: training a classifier in a domain adaptation setting without having access to examples from the test time distribution at training time.
In REF , the authors propose to extrapolate the target data dynamics within a reproducing kernel Hilbert space.
Predicting the future behavior of a time-varying probability distribution
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science Mathematics