@Cedric - I don't think Madeleine's framework includes factor analysis at the moment. Particularly if there is missing data one would have to iteratively alternate between estimating the mean/variance of each feature and the factors. On Jun 26, 2016 5:20 PM, "Cedric St-Jean" <[email protected]> wrote:
> You can also look into Madeleine Udell's LowRankModels.jl. It doesn't > contain factor analysis, but unless I'm mistaken it should be possible to > formulate it by specifying the objective function and regularizers > appropriately > > On Sunday, June 26, 2016 at 7:43:04 PM UTC-4, [email protected] wrote: >> >> I haven't seen anything yet on traditional common factor analysis by >> maximum likelihood. Depending on your problem, you might be able to use >> principal components instead which is implemented in >> MultivariateStats.jl... e.g. in dual-asymptotic framework, simple >> transformations of the first k principal components are consistent >> estimators of the space-spanned by a k-dimensional common factor space. >> >> Cheers, >> >> Colin >> >> On Sunday, 26 June 2016 08:18:27 UTC+10, Jessica Koh wrote: >>> >>> Hello, >>> >>> Is factor analysis currently being developed? >>> >> -- > You received this message because you are subscribed to the Google Groups > "julia-stats" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "julia-stats" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
