Date(s) : 29/09/2017 iCal
14 h 00 min - 15 h 00 min
Thanks to its efficiently exploiting degrees of freedom in large multi-dimensional problems, random matrix theory has today become a compelling field in modern (multi-antenna multi-user multi-cell) wireless communications and is currently making powerful headway into large dimensional signal processing and statistics. With the advent of the big data paradigm, challenging machine learning questions arise, which we claim random matrix theory can address like no other tool before.
In this talk, after a basic introduction and motivation to random matrix theory, we shall discuss our early findings in the theoretical understanding and the resulting practical improvements of kernel spectral clustering and semi-supervised learning for large dimensional data, community detection on large realistic graphs, and shall also briefly discuss neural networks as well as robust statistics applications.
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