San Francisco is the second most densely populated major city in the US after NYC, situated on a 7X7 mile peninsula surrounded by water, with significant numbers of historic buildings and neighborhoods that can't be reasonably torn down and replaced by high density housing.
Blaming high rents solely on city officials and rent control overlooks these realities which shape the economics of real estate in the city.
The number of parameters to be fit or estimated in a statistical or machine learning model will always be a function of the dimensionality. For example, fitting a Gaussian (a Bell Curve) with a fixed variance of 1.0 to an n-dimensional space requires estimating the mean, which is n parameters. If we want to estimate an nXn covariance matrix for our n-dimensional Gaussian, we need to estimate n+n^2 parameters. For linear or logistic regression, we'll need to estimate n+1 parameters, and so on.
The curse of dimensionality is that if you add dimensions but don't add data, your functions will overfit the data because you don't have sufficient samples to estimate your model parameters. The worst-case estimate is that for n dimensions, you will need on the order of 2^n samples. This comes from the combinatoric increase of relative "distances" as dimensions increase.
This 2^n bound assumes that your data has a high degree of uncorrelated variance across all dimensions. In practice, the curse of dimensionality often isn't a problem. This is because most high-dimensionality data residing in an n-dimensional space actually doesn't have uniform variance across all n dimensions, and can be mapped or otherwise transformed into a k-dimensional subspace where k is much smaller than n with a minimal loss of variance.
Dimensionality reduction approaches include principle component analysis (PCA), minimum message length methods (MML), various feature selection approaches, virtually every clustering algorithm. Anything that removes dimensions while retaining the essential information content will do the trick.
I worked at SRI on the CALO project, and built prototypes of the system that was spun off into SIRI. The system uses a simple semantic task model to map language to actions. There is no deep parsing - the model does simple keyword matching and slot filling, and it turns out that with some clever engineering, this is enough to make a very compelling system. It is great to see it launch as a built-in feature on the iPhone.
