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Fisher’s Linear Discriminant Analysis for Weather Data by reproducing kernel Hilbert spaces framework
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Mohammadreza Faridrohani *   , Behdad Mostafaiy , Mohammad Ebrahim Hosseininasab |
| Shahid Beheshti University |
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Abstract: (2164 Views) |
| Recently with science and technology development, data with functional nature are easy to collect. Hence, statistical analysis of such data is of great importance. Similar to multivariate analysis, linear combinations of random variables have a key role in functional analysis. The role of Theory of Reproducing Kernel Hilbert Spaces is very important in this content. In this paper we study a general concept of Fisher’s linear discriminant analysis that extends the classical multivariate method to the case functional data. A bijective map is used to link a second order process to the reproducing kernel Hilbert space, generated by its within class covariance kernel. Finally a real data set related to Iranian weather data collected in 2008 is also treated. |
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| Keywords: Reproducing kernel Hilbert space, linear discriminant analysis, functional data, second order stochastic processes. |
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Full-Text [PDF 306 kb]
(1115 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2020/11/20 | Accepted: 2021/03/17 | Published: 2021/03/18
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