Abstract
Conclusion After struggling with the problem of representing structure in similarity data for over 20 years, I find that a number of challenging problems still remain to be overcome—even in the simplest case of the analysis of a single symmetric matrix of similarity estimates. At the same time, I am more optimistic than ever that efforts directed toward surmounting the remaining difficulties will reap both methodological and substantive benefits. The methodological benefits that I forsee include both an improved efficiency and a deeper understanding of “discovery” methods of data analysis. And the substantive benefits should follow, through the greater leverage that such methods will provide for the study of complex empirical phenomena—perhaps particularly those characteristic of the human mind.
Keywords
Similarity (geometry)Leverage (statistics)Computer scienceRepresentation (politics)Data scienceEconometricsManagement scienceData miningMathematicsMachine learningArtificial intelligencePolitical scienceEconomics
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Publication Info
- Year
- 1974
- Type
- article
- Volume
- 39
- Issue
- 4
- Pages
- 373-421
- Citations
- 481
- Access
- Closed
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Cite This
Roger N. Shepard
(1974).
Representation of Structure in Similarity Data: Problems and Prospects.
Psychometrika
, 39
(4)
, 373-421.
https://doi.org/10.1007/bf02291665
Identifiers
- DOI
- 10.1007/bf02291665