tag:blogger.com,1999:blog-6141980.post4558982630984371052..comments2024-03-20T12:28:35.004-05:00Comments on Nuit Blanche: Scattering Representations for Recognition - implementation -Igorhttp://www.blogger.com/profile/17474880327699002140noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-6141980.post-55490362662405849302012-12-13T06:30:27.772-06:002012-12-13T06:30:27.772-06:00Ivan,
In "vanilla" compressed sensing, ...Ivan,<br /><br />In "vanilla" compressed sensing, the transform is linear (most of the time people use a random projection but not all the time). Mallat's transform is nonlinear hence the comment that the two approaches are orthogonal. <br /><br />In both case, after the transforms have been applied, one can compare the reduced versions using the Euclidian norm. <br /><br />In Mallat's case, the transformed elements are unique if they are a translation or a rotation or... away from one element.<br /><br />In generic compressive sensing, the transformed elements have nearly the same euclidian norm as their larger version.<br /><br />what I like about compressive sensing is this element of randomness as it provides a clear path towards a substition to biological system. But that's just me.<br /> <br /><br />Igor.Igorhttps://www.blogger.com/profile/17474880327699002140noreply@blogger.comtag:blogger.com,1999:blog-6141980.post-24353265062626833142012-12-12T08:34:23.245-06:002012-12-12T08:34:23.245-06:00I listened to the talk and it was brilliant (but t...I listened to the talk and it was brilliant (but the papers are somewhat cumbersome). Why do you think it is orthogonal to compressed sensing? It is a nonlinear transform that is stable with respect to translation and transformations, and has rather surprising and unusual properties, but I do not feel that it is orthogonal :)Anonymoushttps://www.blogger.com/profile/16923482197927845129noreply@blogger.com