Steve,
Principal component analysis is not new, it has a long history, and so
far it is a very long way from being the basis for a complete AGI, let
alone a theory of everything in computer science.
Is there any concrete reason to believe that this particular PCA paper
is doing something that is some kind of quantum leap beyond what can be
found in the (several thousand?) other PCA papers that have already been
written?
To give you an idea of what I am looking for, does the algorithm go
beyond single-level encoding patterns? Can it find patterns of
patterns, up to arbitrary levels of depth? And is there empirical
evidence that it really does find a set of patterns comparable to those
found by the human cognitive mechanism, without missing any obvious cases?
Bloated claims for the effectiveness of some form of PCA turn up
frequently in cog sci, NN and AI. It can look really impressive until
you realize how limited and non-extensible it is.
Richard Loosemore
Steve Richfield wrote:
Y'all,
I have long predicted a coming "Theory of Everything" (TOE) in CS that
would, among other things, be the "secret sauce" that AGI so desperately
needs. This year at WORLDCOMP I saw two presentations that seem to be
running in the right direction. An earlier IEEE article by one of the
authors seems to be right on target. Here is my own take on this...
Form: The TOE would provide a way of unsupervised learning to rapidly
form productive NNs, would provide a subroutine that AGI programs could
throw observations into and SIGNIFICANT patterns would be identified,
would be the key to excellent video compression, and indirectly, would
provide the "perfect" encryption that nearly perfect compression would
provide.
Some video compression folks in Germany have come up with "Principal
Component Analysis" that works a little like clustering, only it also
includes temporal consideration, so that things that come and go
together are presumed to be related, thereby eliminating the
"superstitious clustering" problem of static cluster analysis. There is
just one "catch": This is buried in array transforms and compression
jargon that baffles even me, a former in-house numerical analysis
consultant to the physics and astronomy departments of a major
university. Further, it is computationally intensive.
Teaser: Their article is entitled "A new method for Principal Component
Analysis of high-dimensional data using Compressive Sensing" and applies
methods that *_benefit_* from having many dimensions, rather than being
plagued by them (e.g. as in cluster analysis).
Enter a retired math professor who has come up with some clever
"simplifications" (to the computer, but certainly not to me) to make
these sorts of computations tractable for real-world use. It looks like
this could be quickly put to use, if only someone could translate this
stuff from linear algebra to English for us mere mortals. He also
authored a textbook that Amazon provides peeks into, but in addition to
its 3-digit price tag, it was also rather opaque.
It's been ~40 years since I have had my head into matrix transforms, so
I have ordered up some books to hopefully help me through it. Is there
someone here who is fresh in this area who would like to take a shot at
"translating" some obtuse mathematical articles into English - or at
least providing a few pages of prosaic footnotes to explain their
terminology?
I will gladly forward the articles that seem to be relevant to anyone
who wants to take a shot at this.
Any takers?
Steve Richfield
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