You are right that they are applying a non-linear function (step-function or logistic) to the output of a linear model, and then aggregating. I am not sure if this has equivalent modeling power as a linear model or single logreg model. I suspect it does, but haven't done the math.
I am not aware of results that say that an ensemble of linear or logreg models, trained over partitions of the data, is better than a single linear or logreg model trained over all the data. My understanding was that the opposite was true. Most of the positive results for ensemble methods and bagging are when the base classifier is a decision splits and decision trees, in which case it is clear that the ensemble has more modeling power.
You are correct, it can represent more decision surfaces.
takes off theoretician's hat
In practice, my intuition tells me that aggregating over an ensemble of logreg models trained on subsets of the data is inferior to training a single logreg model over the entire data.
I apologize for being sloppy tho.
Doesn't this depend on how they aggregate the results? In the paper they say that they do majority voting and averaging of the class probabilities, and in both cases the decision surface is not linear (before averaging, in the first case the response of the linear model is passed through a step function, in the second case through a logistic function)
I think what you say is true in a regression setting, if you just average the predictions of the linear models, but here they are talking about classification.