{"pk":30629,"title":"Dimensionality-Reduction and Constraint in Later Vision","subtitle":null,"abstract":"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.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"computational vision"},{"word":"dimensionality-reduction"},{"word":"connectionist"}],"section":"Artificial Intelligence and Simulation II","is_remote":true,"remote_url":"https://escholarship.org/uc/item/785071c9","frozenauthors":[{"first_name":"Eric","middle_name":"","last_name":"Saund","name_suffix":"","institution":"Massachusetts Institute of Technology","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1987-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/30629/galley/20478/download/"}]}