Abstract
A functional classification methodology, based on the Reproducing Kernel Hilbert Space (RKHS) theory, is proposed for discrimination of gene expression profiles. The parameter function involved in the definition of the functional logistic regression is univocally and consistently estimated, from the minimization of the penalized negative log-likelihood over a RKHS generated by a suitable wavelet basis. An iterative descendent method, the gradient method, is applied for solving the corresponding minimization problem, i.e., for computing the functional estimate. Temporal gene expression data involved in the yeast cell cycle are classified with the wavelet-RKHS-based discrimination methodology considered. A simulation study is developed for testing the performance of this statistical classification methodology in comparison with other statistical discrimination procedures.
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Rincón, M., Ruiz-Medina, M.D. Wavelet-RKHS-based functional statistical classification. Adv Data Anal Classif 6, 201–217 (2012). https://doi.org/10.1007/s11634-012-0112-4
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DOI: https://doi.org/10.1007/s11634-012-0112-4
Keywords
- Functional data analysis
- Gene expression profiles
- Penalized logistic regression
- Reproducing kernel Hilbert space
- Wavelet decomposition
- Yeast cell cycle