Sparse kernelized vector quantization with local dependencies

Clustering approaches are very important methods to analyze data sets in an initial unsupervised setting. Traditionally many clustering approaches assume data points to be independent. Here we present a method to make use of local dependencies to improve clustering under guaranteed distortions. Such local dependencies are very common for data generated by imaging technologies with an underlying topographic support of the measured data. We provide experimental results on artificial and real world data of clustering tasks.