Data-distribution-informed Nyström approximation for structured data using vector quantization-based landmark determination

Abstract

We present an effective method for supervised landmark selection in sparse Nyström approximations of kernel matrices for structured data. Our approach transforms structured non-vectorial input data, like graphs or text, into a dissimilarity representation, facilitating the identification of data-distribution-informed landmarks through prototype-based learning. Experimental results indicate competitive approximation quality when compared to existing strategies, showcasing the advantageous impact of incorporating more information into the Nyström landmark selection process. This positions our method as an efficient and versatile solution for large-scale kernel learning.