  In spectral clustering and spectral image segmentation, the data is partioned
starting from a given matrix of pairwise similarities S. the matrix S is
constructed by hand, or learned on a separate training set. In this paper we
show how to achieve spectral clustering in unsupervised mode. Our algorithm
starts with a set of observed pairwise features, which are possible components
of an unknown, parametric similarity function. This function is learned
iteratively, at the same time as the clustering of the data. The algorithm
shows promosing results on synthetic and real data.
