Saturday, 8 February 2014

Accepted contribution at ESANN 2014

Accepted paper on Proximity learning for non-standard big data in the special session on Learning and Modeling Big Data at the ESANN 2014. We discuss the supervised learning and embedding of very large indefinite kernel matrices (generalizes also to arbitrary proximities).

Laplacian eigenmap embedding of ~200.000  protein sequences (40 billion proximities).
The colors refer to the largest 21 ProSite class labels

Related technical reports:

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