[[abstract]]This study considers two clustering criteria to achieve difierent goals of grouping similar curves. These criteria are based on the minimal L2 distance and the maximal functional correlation defined in this study, respectively. Each cluster centers on a subspace spanned by the cluster mean and covariance eigenfunctions of the underlying random functions. Clusters can thus be identified by the subspace projection of curves.[[notice]]補正完畢[[booktype]]電子版[[booktype]]紙
[[abstract]]A covariate adjusted subspace projected functional data classification (SPFC) method is ...
The problem of clustering functional data is addressed. Results on principal points (cluster means f...
We present a new framework for clustering functional data along with a new paradigm for performing m...
[[abstract]]This study considers two clustering criteria to achieve different goals of grouping simi...
[[abstract]]A correlation-based functional clustering method is proposed for grouping curves with si...
A functional clustering (FC) method, "k"-centres FC, for longitudinal data is proposed. The "k"-cent...
[[abstract]]We propose a multivariate k-centers functional clustering algorithm for the multivariate...
[[abstract]]A novel multivariate k-centers functional clustering algorithm for the multivariate func...
Classification is a very common task in information processing and important problem in many sectors...
Functional data can be clustered by plugging estimated regression coefficients from individual curve...
In machine learning, it is common to interpret each data sample as a multivariate vector disregardin...
Functional data clustering procedures seek to identify subsets of curves with similar shapes and est...
We consider the issue of classification of functional data and, in particular, we deal with the prob...
Abstract In this paper, we deal with the problem of curves clustering. We propose a nonparametric me...
The aim of this article is to propose a procedure to cluster functional observations in a subspace ...
[[abstract]]A covariate adjusted subspace projected functional data classification (SPFC) method is ...
The problem of clustering functional data is addressed. Results on principal points (cluster means f...
We present a new framework for clustering functional data along with a new paradigm for performing m...
[[abstract]]This study considers two clustering criteria to achieve different goals of grouping simi...
[[abstract]]A correlation-based functional clustering method is proposed for grouping curves with si...
A functional clustering (FC) method, "k"-centres FC, for longitudinal data is proposed. The "k"-cent...
[[abstract]]We propose a multivariate k-centers functional clustering algorithm for the multivariate...
[[abstract]]A novel multivariate k-centers functional clustering algorithm for the multivariate func...
Classification is a very common task in information processing and important problem in many sectors...
Functional data can be clustered by plugging estimated regression coefficients from individual curve...
In machine learning, it is common to interpret each data sample as a multivariate vector disregardin...
Functional data clustering procedures seek to identify subsets of curves with similar shapes and est...
We consider the issue of classification of functional data and, in particular, we deal with the prob...
Abstract In this paper, we deal with the problem of curves clustering. We propose a nonparametric me...
The aim of this article is to propose a procedure to cluster functional observations in a subspace ...
[[abstract]]A covariate adjusted subspace projected functional data classification (SPFC) method is ...
The problem of clustering functional data is addressed. Results on principal points (cluster means f...
We present a new framework for clustering functional data along with a new paradigm for performing m...