Conference proceeding
Classification of Japanese Kanji using principal component analysis as a preprocessor to an artificial neural network
IJCNN-91-Seattle International Joint Conference on Neural Networks, Vol.i, pp.233-238 vol.1
1991
DOI: 10.1109/IJCNN.1991.155182
Abstract
Applies principal component analysis (PCA) to the problem of classifying handwritten Kanji characters. PCA is a statistical tool which can yield substantial data reduction by representing each pattern in terms of a relatively small subset of orthonormal features (principal components) extracted from the input set. A PCA preprocessor to an artificial neural network has been used to reduce the dimensionality of a set of handwritten Kanji patterns to less than 5% of that of the original images. Reconstructions of the patterns from the preprocessed versions are quite impressive. Preliminary results yield nearly 90% correct classification of exemplars of 40 different Kanji characters, and also indicate that reconstruction requires more information than classification. These results demonstrate the effectiveness of PCA as a preprocessor for neural networks.< >
Details
- Title: Subtitle
- Classification of Japanese Kanji using principal component analysis as a preprocessor to an artificial neural network
- Creators
- S.D. Hyman - National Institutes of HealthT.P. VoglK.T. BlackwellG.S. BarbourJ.M. IrvineD.L. Alkon
- Resource Type
- Conference proceeding
- Publication Details
- IJCNN-91-Seattle International Joint Conference on Neural Networks, Vol.i, pp.233-238 vol.1
- DOI
- 10.1109/IJCNN.1991.155182
- Publisher
- IEEE
- Language
- English
- Date published
- 1991
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984446962202771
Metrics
73 Record Views