2012/2/20 Mathias Verbeke <[email protected]>: > Hi all, > > I have a high-dimensional feature set, where the features originate from > graphs. I was wondering if the use of GraphLasso applies and would be a good > idea in this case? And if it would be, can I then just apply it on the > feature vectors or do I need to input the originating graph structure > somehow as prior knowledge? If not, what would be good alternatives to do > model and feature selection?
How high dimensional is this? GraphLasso works on the empirical covariance matrix which is implemented as an 2D numpy array with shape (n_features, n_features). It won't fit in memory for n_features > 10000 and it might be intractably too long to converge much before that (I haven't tried so I cannot say). Can you tell us more about the data? What is the graph and what are the features? Also can you give more details on what you are trying to achieve? The goal of graph lasso is to identify a sparse graph structure that links nodes according to their covariance (assuming you sample from some time series history for each node / feature). If you already have the graph structure in the first place, why would you use GraphLasso for? -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
