A spectrum of machine learning tasks
Low-dimensional data (e.g.
less than 100 dimensions)
Lots of noise in the data
There is not much structure in
the data, and what structure
there is, can be represented by
a fairly simple model.
The main problem is
distinguishing true structure
from noise.
High-dimensional data (e.g.
more than 100 dimensions)
The noise is not sufficient to
obscure the structure in the
data if we process it right.
There is a huge amount of
structure in the data, but the
structure is too complicated to
be represented by a simple
The main problem is figuring
out a way to represent the
complicated structure so that it
can be learned.