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Cited by 310

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Functional Linear Discriminant Analysis for Irregularly Sampled Curves

James, Gareth M., Hastie, Trevor J.·2001·Journal of the Royal Statistical Society Series B: Statistical Methodology

Abstract

Summary: We introduce a technique for extending the classical method of linear discriminant analysis (LDA) to data sets where the predictor variables are curves or functions. This procedure, which we call functional linear discriminant analysis (FLDA), is particularly useful when only fragments of the curves are observed. All the techniques associated with LDA can be extended for use with FLDA. In particular FLDA can be used to produce classifications on new (test) curves, give an estimate of the discriminant function between classes and provide a one- or two-dimensional pictorial representation of a set of curves. We also extend this procedure to provide generalizations of quadratic and regularized discriminant analysis.

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Metadata

Authors
James, G. M., & Hastie, T. J.
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Published
2001-09-01
Volume
63
Issue
3
Pages
533-550
ISSN
  • 1369-7412 (ISSN-L)
  • 1467-9868
OpenAlex
W1984080040
Cited by
310
References
16
FWCI
5.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Functional Linear Discriminant Analysis for Irregularly Sampled Curves. https://app.cochat.ai/papers/10.1111/1467-9868.00297
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James, G. M., & Hastie, T. J. (2001). Functional Linear Discriminant Analysis for Irregularly Sampled Curves. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/1467-9868.00297

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.