A dimensionality reduction technique for enhancing information context.

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Author
Maurer, Michael Lee
Date
1980-06Advisor
Wilson, L.A.
Second Reader
Hamming, Richard W.
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Show full item recordAbstract
A computer processing technique is advanced which seeks
to retain or improve data information context while reducing
the dimensionality of data representation. Defining
information context as the relative proximity of data
points, a nonlinear transformation is analytically derived
which utilizes Euclidean distance to one or more reference
points to provide a measure of similarity "between data
points. The nonarbitrary reference points are selectively
manipulated to provide, given certain constraints, a unique
mapping from high dimensional space to one or more
dimensions for each point in space. The transformation
process enhances class clustering and interclass separation
in the lower dimensional representation.
Computer processed experimental results are presented of
reduction from 32, 10, and 3 space into 2 space for both
synthetic and real world data. Utilizing a ratio of
intraclass variance to interclass variance as a figure of
merit and as one possible optimization criterion, this
technique yielded a significant ratio improvement in mapping
from higher dimensional space into 2 dimensional space for
all cases examined.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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