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Data-distribution-informed Nyström approximation for structured data using vector quantization-based landmark determination
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DOI：10.1016/j.neucom.2024.128100.png)
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Abstract
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We present an effective method for supervised landmark selection in sparse Nystr & ouml;m 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 Nystr & ouml;m landmark selection process. This positions our method as an efficient and versatile solution for large-scale kernel learning.
Keywords:
Nystr & ouml
m approximation
Landmark selection
Non-vectorial data
Vector quantization
Dissimilarity representation
Indefinite kernel
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JOURNAL OF BIG DATA
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