Tuesday, March 22, 2016

ICASSP poster: Random Projections through multiple optical scattering: Approximating kernels at the speed of light

So our paper got into ICASSP. It's a poster. If you are in Shanghai, Angélique and Laurent will be answering your questions tomorrow on Wednesday, March 23, 16:00 - 18:00. The paper code is BD-P1.5, it is in the Big Data session and located in poster Area G 

Go ask Angélique and Laurent some questions ! 
The best questions and answers will be featured on Nuit Blanche

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.
Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: