Learning representative exemplars using one-class Gaussian process regression

An exemplar is an observation that represents a group of similar observations. Exemplars from data are examined to divide entire heterogeneous data into several homogeneous subgroups, wherein each subgroup is represented by an exemplar. With its inherent sparsity, an exemplar-based learning model provides a parsimonious model to represent or cluster large-scale data. A novel exemplar learning method using one-class Gaussian process (GP) regression is proposed in this study. The proposed method constructs data distribution support from one-class GP regression using automatic relevance determination prior and heterogeneous GP noise. Exemplars that correspond to the basis vectors of the constructed support function are then automatically located during the training process. The proposed method is applied to various data sets to examine its operability, characteristics of data representation, and cluster analysis. The exemplars of some real data generated by the proposed method are also reported.