Hi Jan, I can't access that link, but I found this on [wikipeida](http://en.wikipedia.org/wiki/Errors-in-variables_models). Assuming it's talking about the same thing, I think they're talking about two different problems. One of the motivations in the papers I've found for bilinear regression is providing a way to enforce sparsity in the model: if each sample is an $A \times B$ matrix,then simply flattening it to a vector and doing linear regression gives a model with $AB$ coefficients, whereas bilinear regression with $m$ pairs of $(u_i,v_i)$ has $m (A+B)$ coefficients. This reduction has been obtained by some loss of flexibility, but the reports I've read seem to indicated that overall it does well at preventing over-fitting. If there is a connection I'd be very interested to know more about it.

I'm going to have a go at using bilinear regression on the El Nino dataset, so the entry is partly just a place to store my calculations of the derivatives -- which are done for the way more complicated logistic regression model in the papers I've found.