Proceedings of the Annual Meeting of the Cognitive Science Society

A computational tool is presented for maintaining and accessing knowledge of certain types of constraint in data: when data samples in an n-dimensional feature space are all constrained to lie on an m-dimensional surface, m < n, they can be encoded more concisely and economically in terms of location on the m-dimensional surface than in terms of the n feature coordinates. The receding of data in this way is called dimensionality-reduction. Dimensionality-reduction may prove a useful computational tool relevant to later visual processing. Examples are presented from shape analysis.