Comments (2)
No surveys that I know of unfortunately, but here's a list off the top of my head:
- I've heard people say you can use the close connection between LDA and Logistic Regression to initialize one with the other (I haven't thought about too much about speed / efficiency trade-offs here)
- In the non-convex case, there's the "famous" k-means++
- the starting guess for the approximation to the Hessian in BFGS can have significant consequences for the convergence of the algorithm
- you can exploit the close connection between W-OLS and Logistic Regression to infer things about variable addition / dropping, which is related to multiple refits (see the Logistic Regression chapter in Elements of Statistical Learning)
Ultimately, I think the biggest bang will come from smart initializations when refitting a model, but I'd like to include at least a little thought on initializations from scratch as well.
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As we discussed earlier, I think this is a really cool idea, and I'm glad to be part of the discussion.
As a novice to this (and for the purposes of furthering a discussion), do you know any good surveys of what the academically well-grounded
things look like and/or some higher-level discussions of the benefits of Smart Initialization™?
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