Ben, On 7/22/08, Benjamin Johnston <[EMAIL PROTECTED]> wrote: > > > You are confusing what PCA now is, and what it might become. I am more >> interested in the dream than in the present reality. >> > > That is like claiming that multiplication of two numbers is the answer to > AGI, and then telling any critics that they're confusing what multiplication > is now with what multiplication may become.
*Restating (not copying) my original posting, the challenge of effective unstructured learning is to utilize every clue and NOT just go with static clusters, etc. This includes temporal as well as positional clues, information content, etc. PCA does some but certainly not all of this, but considering that we were talking about clustering here just a couple of weeks ago, ratcheting up to PCA seems to be at least a step out of the basement.* ** *I think that perhaps I mis-stated or was misunderstood in my "position". No one has "the answer" yet, but given recent work, I think that perhaps the problem can now be stated. Given a problem statement, it (hopefully) should be "just some math" to zero in on the solution. OK...* ** *Problem Statement: What are the optimal functions, derived from real-world observations of past events, the timings of their comings and goings, and perhaps their physical association, to extract each successive parameter containing the maximum amount of information (in a Shannon sense) usable in reconstructing the observed inputs. IMHO these same functions will be exactly what you need to recognize what is happening in the world, what you need to act upon, which actions will have the most effect on the world, etc. PCA is clearly NOT there (e.g. it lacks temporal consideration), but seems to be a step closer than anything else on the horizon. Hopefully, given the "hint" of PCA, we can follow the path.* ** You should find an explanation of PCA in any elementary linear algebra or statistics textbook. It has a range of applications (like any transform), but it might be best regarded as an/the elementary algorithm for unsupervised dimension reduction. *Bingo! However, it still fails to consider temporal clues, unless of course you just consider these to be another dimension.* When PCA works, it is more likely to be interpreted as a comment on the underlying simplicity of the original dataset, rather than the power of PCA itself. *Agreed, but so far, I haven't seen any solid evidence that the world is NOT simple, though it appears pretty complex until you understand it. Thanks for making me clarify my thoughts.* ** *Steve Richfield* ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=108809214-a0d121 Powered by Listbox: http://www.listbox.com
