The extension of the largest generalized-eigenvalue based distance metric ) in arbitrary feature spaces to classify composite data points
Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogen...
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| Format: | Article |
| Language: | English |
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Korea Genome Organization
2019-11-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-4-e39.pdf |