tag:blogger.com,1999:blog-6141980.post8848670262684717080..comments2024-03-20T12:28:35.004-05:00Comments on Nuit Blanche: Thesis: Fast Algorithms on Random Matrices and Structured Matrices by Liang ZhaoIgorhttp://www.blogger.com/profile/17474880327699002140noreply@blogger.comBlogger1125tag:blogger.com,1999:blog-6141980.post-59510053660011431762017-05-16T04:17:42.032-05:002017-05-16T04:17:42.032-05:00I'll read that paper later:
On this video abou...I'll read that paper later:<br />On this video about lasso:<br />https://youtu.be/Hn8NtydkeDs<br /><br />I made this comment:<br /><br />"You are saying that the reconstructed data lies on an L1 manifold. You can learn a manifold using say a single layer neural network autoencoder. Then to reconstruct you can invert the dimensionally reduced data, get the autoencoder to correct it, send it back through the dimensional reduction and correct only the reduced aspect. Just bounce back and forth between the two.<br />Or you could set the manifold to be the moving average of the data which is a very easy manifold to correct to and bounce between the two. Anyway: https://drive.google.com/open?id=0BwsgMLjV0BnhOGNxOTVITHY1U28"SeanVNhttps://www.blogger.com/profile/05967727000105480078noreply@blogger.com