Approximating Kernels at the Speed of Light

It could be one of those Hamming's time blog entries, except it's not. There will be some announcements about this in the coming future. In the meantime and without further ado, here is the preprint:

Random Projections through multiple optical scattering: Approximating kernels at the speed of light  by Alaa Saade, Francesco Caltagirone, Igor Carron, Laurent Daudet, Angélique Drémeau, Sylvain Gigan, Florent Krzakala

Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix, or to use a different, structured matrix to reduce the computational and memory costs. Here, we overcome this difficulty by proposing an analog, optical device, that performs the random projections literally at the speed of light without having to store any matrix in memory. This is achieved using the physical properties of multiple coherent scattering of coherent light in random media. We use this device on a simple task of classification with a kernel machine, and we show that, on the MNIST database, the experimental results closely match the theoretical performance of the corresponding kernel. This framework can help make kernel methods practical for applications that have large training sets and/or require real-time prediction. We discuss possible extensions of the method in terms of a class of kernels, speed, memory consumption and different problems.

Some of my co-authors ( sylvain Gigan, Laurent Daudet, Alaa Saade , Krzakala Florent ) mentioned the news of the preprint on their twitter feed:


Need fast random projections for your favorite machine learning algorithm? We made them (literally) at the speed of light! arXiv:1510.06664

— Krzakala Florent (@KrzakalaF) October 23, 2015 ‏

We are building hardware to speed up Machine Learning w/ light https://t.co/I7aeWubAFt @IgorCarron @Laurent_Daudet @KrzakalaF @sylvaingigan

— Alaa Saade (@saade_alaa) October 23, 2015


"Random Projections through multiple optical scattering: Approximating kernels at the speed of light"@IgorCarron @KrzakalaF @Laurent_Daudet

— sylvain Gigan (@sylvaingigan) October 23, 2015


Credit: NASA/NOAA

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