Date of Original Version
Abstract or Description
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algorithms to operate on groups of data points. We suggest treating a group of data points as a set of i.i.d. samples from an underlying feature distribution for the group. Our approach is to generalize kernel machines from vectorial inputs to i.i.d. sample sets of vectors. For this purpose, we use a nonparametric estimator that can consistently estimate the inner product and certain kernel functions of two distributions. The projection of the estimated Gram matrix to the cone of semi-definite matrices enables us to employ the kernel trick, and hence use kernel machines for classification, regression, anomaly detection, and low-dimensional embedding in the space of distributions. We present several numerical experiments both on real and simulated datasets to demonstrate the advantages of our new approach.