The discovery of structural form.

@article{citeulike:3080763, abstract = {Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than" or "better than") respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.}, address = {Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213.}, author = {Kemp, Charles and Tenenbaum, Joshua B B.}, citeulike-article-id = {3080763}, doi = {10.1073/pnas.0802631105}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {machinelearning}, month = {July}, posted-at = {2008-09-02 01:43:16}, priority = {4}, title = {The discovery of structural form.}, url = {http://dx.doi.org/10.1073/pnas.0802631105}, year = {2008} }

TY - JOUR ID - citeulike:3080763 L3 - citeulike-article-id:3080763 N2 - Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than" or "better than") respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development. JF - Proceedings of the National Academy of Sciences of the United States of America SN - 1091-6490 AD - Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213. TI - The discovery of structural form. KW - machinelearning AU - Kemp, Charles AU - Tenenbaum, Joshua B PY - 2008/07/31/ UR - http://dx.doi.org/10.1073/pnas.0802631105 ER -