Type of Document Master's Thesis Author McGraw, John M. Author's Email Address jmcgraw@icam.vt.edu URN etd-10252001-104137 Title An Investigation into Classification of High Dimensional Frequency Data Degree Master of Science Department Statistics Advisory Committee
Advisor Name Title Smith, Eric P. Committee Chair Burns, John A. Committee Member Woodall, William H. Committee Member Keywords
- Multivariate Normal
- Frequency Response
- Confidence Interval
- Data Analysis
- Correlation
- Maximum Likelihood
Date of Defense 2001-10-23 Availability unrestricted Abstract We desire an algorithm to classify a physical object in``real-time" using an easily portable probing device. The probe excites
a given object at frequencies from 100 MHz up to 800 MHz at intervals
of 0.5 MHz. Thus the data used for classification is the
1400-component vector of these frequency responses.
The Interdisciplinary Center for Applied Mathematics (ICAM) was asked to
help develop an algorithm and executable computer code for the probing
device to use in its classification analysis. Due to these and other
requirements, all work had to be done in Matlab. Hence a significant portion
of the effort was spent in writing and testing applicable Matlab code which
incorporated the various statistical techniques implemented.
We offer three approaches to classification: maximum log-likelihood
estimates, correlation coefficients, and confidence bands. Related work
included considering ways to recover and exploit certain symmetry
characteristics of the objects (using the response data). Present
investigations are not entirely conclusive, but the correlation
coefficient classifier seems to produce reasonable and consistent results.
All three methods currently require the evaluation of the full
1400-component vector. It has been suggested that unknown portions of
the vectors may include extraneous and misleading information, or
information common to all classes. Identifying and removing the respective
components may be beneficial to classification regardless of method. Another
advantage of dimension reduction should be a strengthening of mean and
covariance estimates.
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