E.g. Histogram approaches don't tolerate rotation in depth. Learning in high-dimensional space. There is an exponential dependence on more data as the dimensionality grows. With reduced dimensionality, challenge to deal with novel objects. Best Representation is the one most suited to the task at hand.
Representation is representation of similarities or relational qualities.
Edelman uses this approach towards visual recognition of shapes. Objects with comparable shapes are mapped into the same neighborhood of an internal shape space. Develop computational mechanisms to support recognition-related tasks based on a navigation metaphor. First implemented as a Radial Basis function approximation network - given several views of a shape the network was trained to produce roughly constant response to other views of the same object. In Edelman's Chorus model, the shape space consists of outputs of functional modules tuned to a range of views of an object.