New article about conversion of proximity data published

Our article

Metric and non-metric proximity transformations at linear costs

is now online available at Elsevier / Neurocomputing.

Highlights

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We propose a linear time and memory efficient approach for converting low rank dissimilarity matrices to similarity matrices and vice versa.

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Our approach is applicable for proximities obtained from non-metric proximity measures (indefinite kernels, non-standard dissimilarity measures).

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The presented approach also comprises a generalization of Landmark MDS – the presented approach is in general more accurate and flexible than Landmark MDS.

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We provide an alternative derivation of the Nyström approximation together with a convergence proof, also for indefinite kernels not given in the workshop paper as a core element of the approach.