Kernel relevant component analysis for distance metric learning
Defining a good distance measure between patterns is of crucial importance in many classification and clustering algorithms. Recently, relevant component analysis (RCA) is proposed which offers a simple yet powerful method to learn this distance metric. However, it is confined to linear transforms in the input space. In this paper, we show that RCA can also be kernelized, which then results in significant improvements when nonlinearities are needed. Moreover, it becomes applicable to distance metric learning for structured objects that have no natural vectorial representation. Besides, it can be used in an incremental setting. Performance of this kernel method is evaluated on both toy and real-world data sets with encouraging results.