EPSRC Reference: |
GR/K71721/01 |
Title: |
SINGULAR DISCRIMINANT ANALYSIS |
Principal Investigator: |
Brown, Professor P |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Sch of Maths Statistics & Actuarial Sci |
Organisation: |
University of Kent |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 April 1995 |
Ends: |
31 January 1996 |
Value (£): |
26,302
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EPSRC Research Topic Classifications: |
Statistics & Appl. Probability |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
The aim is to develop new and improved methods for discrimination problems where the number of measured variables (p) is large compared with the number of training samples (n). The research is motivated by a range of chemometric applications but particularly the use on near-infrared (NIR) spectrophotometers to identify chemicals and foodstuffs. Practical industrial examples of this application, where p=700 and n=100 is typical, will be available for study. Standard discriminant analysis fails for the problem because of the difficulty of estimation within groups covariance matrix with p>n. The approaches to be investigated include: imposing appropriate structure on the covariance matrix; specifying a proper prior distribution for the covariance matrix in a Bayesian analysis; directly modelling probabilities of group inclusion given measured variables; and applying soft modelling methods such as continuum regression. For all but the last approach the Bayesian framework will be used, the emphasis being on explicit incorporation of available prior information.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.kent.ac.uk |