Classification model with collinear grouping of features
ABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in pr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2022.2129133 |
| Summary: | ABSTRACTPattern recognition procedures operate on data represented as sets of multidimensional feature vectors. A small sample of data appears when the dimension of the feature vectors (number of features) is much larger than the number of feature vectors (objects). Small datasets often emerge in practice, for example in genetics. The design of classification or prognostic models on small data sets requires the development of new types of methods. Methods based on L1 geometry can play an important role in this regard. |
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| ISSN: | 2475-1839 2475-1847 |