We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure coreplatform@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Chapter 2 presents several strategies to exploit sparsity in the parameters being estimated in order to obtain better estimates and accelerate convergence, two advantages of paramount importance when dealing with real problems requiring the estimation of many parameters. In these cases, the classical adaptive filtering algorithms exhibit a slow and often unacceptable convergence rate. In this chapter, many algorithms capable of exploiting sparse models are presented. Also, the two most widely used approaches to exploit sparsity are presented, and their pros and cons are discussed. The first approach explicitly models sparsity by relying on sparsity-promoting regularization functions. The second approach utilizes updates proportional to the magnitude of the coefficient being updated, thus accelerating the convergence of large magnitude coefficients. After reading this chapter, the reader will not only obtain a deeper understanding of the subject but also be able to adapt or develop algorithms based on his own needs.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.