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).
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| Laplacian eigenmap embedding of ~200.000 protein sequences (40 billion proximities). The colors refer to the largest 21 ProSite class labels |
Related technical reports:
- Large scale Nyström approximation for non-metric similarity and dissimilarity data
- Data analysis of (non-)metric (dis-)similarities at linear costs
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