Not sure how to handle the data representation (masked arrays make sense),
but you probably want to look into matrix
completion<http://en.wikipedia.org/wiki/Matrix_completion>.
In particular, a visitor at Knewton recently discussed his experience
implementing singular value
projection<http://books.nips.cc/papers/files/nips23/NIPS2010_0682.pdf>
.
> Maybe it would only operate on sparse arrays, and infer that the values
> which are missing are the ones to be imputed ("recommended")? But not
> supporting dense arrays would basically be the opposite of other modules in
> sklearn, where dense input is default. Maybe someone can comment on this?
>
> I don't know how well this lines up with the existing API/functionality and
> the future directions there, but how to deal with the missing values is
> probably the primary concern for implementing CF algorithms in sklearn IMO.
>
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