Classification of reduction invariants with improved backpropagation
Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal co...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2002-01-01
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| Series: | International Journal of Mathematics and Mathematical Sciences |
| Online Access: | http://dx.doi.org/10.1155/S0161171202006117 |
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| Summary: | Data reduction is a process of feature extraction that transforms
the data space into a feature space of much lower dimension
compared to the original data space, yet it retains most of the
intrinsic information content of the data. This can be done by
using a number of methods, such as principal component analysis
(PCA), factor analysis, and feature clustering. Principal
components are extracted from a collection of multivariate cases
as a way of accounting for as much of the variation in that
collection as possible by means of as few variables as possible.
On the other hand, backpropagation network has been used
extensively in classification problems such as XOR problems,
share prices prediction, and pattern recognition. This paper
proposes an improved error signal of backpropagation network for
classification of the reduction invariants using principal
component analysis, for extracting the bulk of the useful
information present in moment invariants of handwritten digits,
leaving the redundant information behind. Higher order
centralised scale- invariants are used to extract features of
handwritten digits before PCA, and the reduction invariants are
sent to the improved backpropagation model for classification
purposes. |
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| ISSN: | 0161-1712 1687-0425 |