Achieving near perfect classification for functional data

By Aurore Delaigle, Peter Hall. 2012

203 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/19d27eb9d43514ea9cce5b65ca46092fcb09588b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. H. Muller
2. Peter Hall
3. Antonio Cuevas
4. R. Dennis Cook
5. Aurore Delaigle
6. José R. Berrendero
7. José L. Torrecilla
8. Zhihua Su

In Molecular Biology

1. H. Muller
2. Peter Hall
3. Aurore Delaigle
4. Jeng-Min Chiou
5. Jane-ling Wang
6. Antonio Cuevas
7. José R. Berrendero
8. José L. Torrecilla

In Statistics and Probability

1. H. Muller
2. Peter Hall
3. Jeng-Min Chiou
4. Jane-ling Wang
5. Aurore Delaigle
6. R. Dennis Cook
7. Antonio Cuevas
8. José R. Berrendero

Citations in the corpus

• Selected statistical methods of data analysis for multivariate functional data (2018, background): “…popular, e.g. in the cluster analysis (Jacques and Preda 2013; James and Sugar 2003; Peng and Müller 2008), classification (Chamroukhi et al. 2013; Delaigle and Hall 2012; Mosler and Mozharovskyi 2015; Rossi and Villa 2006) and regression (Ferraty et al. 2012; Goia and Vieu 2014; Kudraszow and…”
• Methodology and theory for partial least squares applied to functional data (2012, methodology): “…Ferraty and Vieu (2006) use it to define a semi-metric for nonparametric functional predictors or classifiers; Escabias, Aguilera and Valderrama (2007) employ PLS with logit regression; Preda, Saporta and Lévéder (2007) and Delaigle and Hall (2012) use it for functional data classification.”
• Classification Using Censored Functional Data (2013, methodology): “Third, we show that the perfect classification property of the linear discriminant classifier described by Delaigle and Hall (2012) can be extended from the context of conventional functional datasets to the setting of fragments of functions.”
• Functional Data Analysis (2016, background): “Theoretical support and a notion of “perfect classification” standing for asymptotically vanishing misclassification probabilities has been introduced in Delaigle & Hall (2012) for linear and Delaigle & Hall (2013) for quadratic functional classification.”
• On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification (2015, methodology): “We are also able to shed some light on the interesting phenomenon of “near perfect classification”, discussed by Delaigle and Hall (2012a). This phenomenon does not appear (except for trivial or artificial cases) in the classical finite-dimensional classification theory.”
• Peter Hall, functional data analysis and random objects (2016, methodology): “In the 2012 paper (Delaigle and Hall, 2012b), a functional version of Fisher’s linear discriminant analysis was considered.”
• Envelopes and partial least squares regression (2013, background): “For instance, Delaigle and Hall (2012) found that PLS does very well in classification problems for functional data and we conjecture that an extension of envelope methodology will offer gains over”
• A survey of functional principal component analysis (2014, unknown): “2007 , Delaigle & Hall 2012 ) .”