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Filtering‐based approaches for functional data classification

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Abstract Because of its many practical applications, classifying functional data has received considerable attention over the last decades. Most classification approaches for functional data are extended from those for multivariate data. During the extension, two strategies, namely filtering and regularization, have commonly been employed to tackle the issues raised by the fact that functional data are intrinsically infinite‐dimensional. Because of space limitations, we focus on the filtering methods in this review. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Sample plots of simulated data
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Forty‐four Fourier‐transform infrared (FT‐IR) spectra of the Wine Samples dataset
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Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data

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