Ranking, clustering and data visualisation
In this chapter we conclude our presentation of kernel-based pattern analysis algorithms by discussing three further common tasks in data analysis: ranking, clustering and data visualisation.
Ranking is the problem of learning a ranking function from a training set of ranked data. The number of ranks need not be specified though typically the training data comes with a relative ordering specified by assignment to one of an ordered sequence of labels.
Clustering is perhaps the most important and widely used method of unsupervised learning: it is the problem of identifying groupings of similar points ...