*[I am on quasi-vacation this week, so only posting irregularly.]*

I (finally—or really for the N-th time, because I keep forgetting) understood the basis of E-M algorithms for optimizing (what I call) marginalized likelihoods in latent-variable models. I then worked out the equations for the E-M step for factor analysis, and a generalization of factor analysis that I hope to use in my project with Christina Eilers (MPIA).

*Imagine my concern* when I got a different update step than I find in the writings of my friend and mentor Sam Roweis (deceased), who is the source of all knowledge, as far as I am concerned! I spent a lot of time looking up stuff on the web, and most things agree with Roweis. But finally I found this note by Andrew Ng (Stanford / Coursera), which agrees with me (and disagrees with Roweis).

If you care about the weeds, the conflict is between equation (8) in those Ng notes and page 3 of these Roweis notes. It is a subtle difference, and it takes some work to translate notation. I wonder if the many documents that match Roweis derive from (possibly unconscious) propagation from Roweis, or whether the flow is in another direction, or whether it is just that the mistake is an easy one to make? Oddly, Ng decorates his equation (8) with a warning about an error you can easily make, but it isn't the error that Roweis made.

So much of importance in computer science and machine learning is buried in lecture notes and poorly indexed documents in user home pages. This is not a good state of affairs!