Filtering‐based approaches for functional data classification

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 regularizati...

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Bibliographic Details
Published in:Wiley interdisciplinary reviews. Computational statistics 2020-07, Vol.12 (4), p.e1490-n/a
Main Authors: Jiang, Ci‐Ren, Chen, Lu‐Hung
Format: Article
Language:English
Subjects:
Classification
Clustering
Data
Data analysis
dimension reduction
Dimensional analysis
Filtration
functional data
generalized linear model
Graphical methods
linear discriminant analysis
Multivariate analysis
Regularization
Statistical methods
support vector machine
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Summary: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 Filtering‐based approaches for functional data classification consist of two steps: (i) select (or estimate) a proper set of basis functions and (ii) perform a traditional classification approach on the corresponding Fourier coefficients.
ISSN:1939-5108
1939-0068
DOI:10.1002/wics.1490