The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recently, there has been a lot of focus on penalized least squares problems for noisy sparse signal estimation. The penalty induces sparsity and a very common choice has been the convex norm. However, to improve sparsity and reduce the biases associated with the norm, one must move to non-convex penalties such as the norm . In this paper we present a novel cyclic descent...
Recently, a lot of attention has been given to penalized least squares problem formulations for sparse signal reconstruction in the presence of noise. The penalty is responsible for inducing sparsity, where the common choice used is the convex l1 norm. While an l0 penalty generates maximum sparsity it has been avoided due to lack of convexity. With the hope of gaining improved sparsity but more computational...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.