Clustering for Sparsely Sampled Functional Data

By Gareth M. James, Catherine A. Sugar. 2003

561 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/03a97ed569bf7e49567c52be228aa826e39a1b53

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Citations in the corpus

Functional Modelling and Classification of Longitudinal Data. (2005, methodology): “In the framework of functional random effects models, sparse data can be handled by incorporating random effects into models based on B-splines (Shi et al., 1996; Rice & Wu, 2000; James et al., 2001; James & Sugar, 2003).”
Achieving near perfect classification for functional data (2012, methodology): “…who suggested linear discriminant analysis, Ferraty and Vieu (2003), who constructed classifiers based on kernel estimators of prior probabilities, James and Sugar (2003), who introduced clustering methods for functional data, Hall et al. (2001), Glendinning and Herbert (2003), Huang and Zheng…”
Clustering of time-course gene expression data using a mixed-effects model with B-splines (2003, methodology): “Motivated by the work of Rice and Wu (2001) and James and Suger (2002), Luan and Li (2002) and Li et al. (2002) developed the mixed-effects model for the time-course gene expression data using B-splines (De Boor, 1978), treating gene expression level as a continuous function”
Classification Using Censored Functional Data (2013, background): “1, through the wheat data described by Kalivas (1997). The data Xi, for i = 1, .”
Selected statistical methods of data analysis for multivariate functional data (2018, methodology): “Methods of functional data analysis are becoming increasingly 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…”
High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease. (2007, background): “Recently, several algorithms treating time as a continuous variable have been introduced to deal with this issue, including spline-based methods such as linear, cubic and B-splines (21-29).”
Functional Data Analysis (2016, methodology): “An example is James & Sugar (2003), who applied functional clustering models based on Gaussian mixture distributions to the natural cubic spline basis coeﬃcients, with emphasis on clustering sparsely sampled functional data.”
Functional data analysis for volatility (2011, background): “One application is discriminant and cluster analysis to determine or characterize particular time-dynamic patterns (James and Sugar 2003, Müller 2005).”
Functional data clustering: a survey (2013, methodology): “Filtering methods consist of a ﬁrst step, quoted as ﬁltering step in James and Sugar (2003), in which the dimension of data is reduced.”
Model-based clustering for multivariate functional data (2013, methodology): “…study, Funclust is compared to three challengers dedicated to the clustering of univariate functional data: FunHDDC (Bouveyron and Jacques, 2011) and k -centres (kCFC, Chiou and Li (2007)) which are the closest methods and fclust (James and Sugar, 2003) which is known to be a good challenger.”
Yield and Price Forecasting for Stochastic Crop Decision Planning (2010, methodology): “k=1 π̂k ( μ̂k(t)± Φ−1(1− α 2 )υ̂k(t) ) , (13) where Φ−1 is the inverse cumulative density function and υk is the diagonal of the covariance matrix of the k cluster.”