Hi all, Following a conversation on irc, I would like to suggest the non-negative least squares algorithm for sklearn. This much older method has some advantages over l1 regularisation (lasso) when the signs of the terms are known. This is particularly the case when sparse recovery is required. One particularly large advantage is that no parameters are needed for its use but you may also get sparser results than lasso gives by forcing non-negativity.
This recent paper http://arxiv.org/abs/1205.0953 summarises some of the benefits much better than I could. Best wishes, Raphael ------------------------------------------------------------------------------ Don't let slow site performance ruin your business. Deploy New Relic APM Deploy New Relic app performance management and know exactly what is happening inside your Ruby, Python, PHP, Java, and .NET app Try New Relic at no cost today and get our sweet Data Nerd shirt too! http://p.sf.net/sfu/newrelic-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
