The Wikipedia article on PCA cites papers that show K-means clustering and PCA to be in a certain sense equivalent-- from what I read so far, the idea is that clustering is simply extracting discrete versions of the continuous variables that PCA extracts.
http://en.wikipedia.org/wiki/Principal_component_analysis#Relation_to_K-means_clustering Does that settle it? On Wed, Jul 23, 2008 at 2:21 AM, Steve Richfield <[EMAIL PROTECTED]> wrote: > Ben, > > On 7/22/08, Benjamin Johnston <[EMAIL PROTECTED]> wrote: >>> >>> /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./ >> >> You should actually try PCA on real data before getting too excited about >> it. > > > Why, as I have already conceded that virgin PCA isn't a solution? I would > expect it to fail in expected ways until it is repaired/recreated to address > known shortcomings, e.g. that it works on linear luminosity rather than > logarithmic luminosity. In short, I am not ready for data yet - until I am > first tentatively happy with the math. > >> >> Clustering and dimension reduction are related, but they are different and >> equally valid techniques designed for different purposes. > > > Perhaps you missed the discussion a couple of weeks ago, where I listed some > of the UNstated assumptions in clustering that are typically NOT met in the > real world, e.g.: > 1. It presumes that cluster exist, whether or not they actually do. > 2. It is unable to deal with data that has wildly different importance. > 3. Corollary to 2 above, any random input completely trashes it. > 4. It is designed for neurons/quantities where intermediate values have > special significance, rather than for fuzzy indicators that are just midway > between TRUE and FALSE. This might be interesting for stock market analysis, > but has no (that I know of) parallel in our own neurons. > >> >> It is absurd to say that one is "ratcheting up" from the other. > > > I agree that they do VERY different jobs, but I assert that the one that > clustering does has nothing to do with NN, AGI, or most of the rest of the > real world. I short, I am listening and carefully considering all arguments > here, but in this case, I am still standing behind my "ratcheting up" > statement, at least until I hear a better challenge to it. > > Steve Richfield > > ________________________________ > agi | Archives | Modify Your Subscription ------------------------------------------- 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
