Kernel principal component analysis in data stream mining tasks
Електронного архіву Харківського національного університету радіоелектроніки (Open Access Repository of KHNURE)
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Kernel principal component analysis in data stream mining tasks
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| Creator |
Bodyanskiy, Ye. V.
Deineko, A. O. Eze, F. M. Shalamov, M. O. |
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| Subject |
data stream
self-learning paradigm |
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| Description |
Currently, self-learning systems of computational intelligence [1, 2] and, above all , artificial neural networks (ANN ), that tune their parameters without a teacher on the basis of the self-learning paradigm [3], are widely used in solving various problems of Data Mining, Exploratory Data Analysis etc. Among these tasks, most frequently encountered in the Text Mining, Web Mining, Medical Data Mining, it be can mentioned the problem of compression of large data sets, for whose solution principal component analysis (PCA) is widely used, which consists in the orthogonal projection of input data vectors from the original n-dimensional space in the m- dimensional space of reduced dimensionality
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| Date |
2016-11-09T09:32:50Z
2016-11-09T09:32:50Z 2016 |
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| Type |
Article
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| Identifier |
http://openarchive.nure.ua/handle/document/3434
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| Language |
en
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