Hi Olivier,
Thanks for the fast reply!
> 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).
I would have around 10000 features. I'm working on a sentence
classification problem,
where the graphs represent sentences, so the features are linguistic
properties and other
values deduced from that.
>
> 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?
>
I would like to do feature selection, to reduce the number of dimensions,
and was thinking to take the graph structure into account for that. Would
you
have any ideas on what would be the best way to do that (with or without
considering the graph structure)?
Thanks,
Mathias
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