article

SINBAD automation of scientific discovery: From factor analysis to theory synthesis

Authors:

Oleg V. FavorovAuthors Info & Claims

Natural Computing: an international journal, Volume 3, Issue 2

Pages 207 - 233

https://doi.org/10.1023/B:NACO.0000027756.50327.26

Published: 13 May 2004 Publication History

Abstract

Modern science is turning to progressively more complex and data-rich subjects, which challenges the existing methods of data analysis and interpretation. Consequently, there is a pressing need for development of ever more powerful methods of extracting order from complex data and for automation of all steps of the scientific process. Virtual Scientist is a set of computational procedures that automate the method of inductive inference to derive a theory from observational data dominated by nonlinear regularities. The procedures utilize SINBAD – a novel computational method of nonlinear factor analysis that is based on the principle of maximization of mutual information among non-overlapping sources, yielding higher-order features of the data that reveal hidden causal factors controlling the observed phenomena. The procedures build a theory of the studied subject by finding inferentially useful hidden factors, learning interdependencies among its variables, reconstructing its functional organization, and describing it by a concise graph of inferential relations among its variables. The graph is a quantitative model of the studied subject, capable of performing elaborate deductive inferences and explaining behaviors of the observed variables by behaviors of other such variables and discovered hidden factors. The set of Virtual Scientist procedures is a powerful analytical and theory-building tool designed to be used in research of complex scientific problems characterized by multivariate and nonlinear relations.

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Cited By

Okan Sakar CKursun O(2012)A method for combining mutual information and canonical correlation analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.02039:3(3333-3344)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.020
Gumus EKilic NSertbas AUcan O(2010)Evaluation of face recognition techniques using PCA, wavelets and SVMExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.02.07937:9(6404-6408)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1016/j.eswa.2010.02.079

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cover image Natural Computing: an international journal

Natural Computing: an international journal Volume 3, Issue 2

2004

102 pages

ISSN:1567-7818

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Copyright © Copyright © 2004 Kluwer Academic Publishers.

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Kluwer Academic Publishers

United States

Publication History

Published: 13 May 2004

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Cited By

Okan Sakar CKursun O(2012)A method for combining mutual information and canonical correlation analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.02039:3(3333-3344)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.020
Gumus EKilic NSertbas AUcan O(2010)Evaluation of face recognition techniques using PCA, wavelets and SVMExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.02.07937:9(6404-6408)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1016/j.eswa.2010.02.079

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