Add to ORKGThe problem of curve clustering when curves are misaligned is considered. A novel algorithm is described, which jointly clusters and aligns curves. The proposed procedure efficiently decouples amplitude and phase variability; in particular, it is able to detect amplitude clusters while simultaneously disclosing clustering structures in the phase, pointing out features that can neither be captured by simple curve clustering nor by simple curve alignment. The procedure is illustrated via simulation studies and applications to real data.Functional data analysis Curve alignment Curve clustering k-mean algorithm
We develop a new method to locally cluster curves and discover functional motifs, i.e.~typical ``sha...
Abstract In this paper, we deal with the problem of curves clustering. We propose a nonparametric me...
MICA enables the automatic synchronization of discrete data curves. To this end, characteristic poin...
The problem of curve clustering when curves are misaligned is considered. A novel algorithm is descr...
A problem, often encountered in functional data analysis, is misalignment of the data. Many methods ...
We consider the issue of classification of functional data and, in particular, we deal with the prob...
The problem of detecting clusters is a common issue in the analysis of functional data and some int...
In this paper we present a family of algorithms that can simultaneously align and cluster sets of mu...
Functional data that are not perfectly aligned in the sense of not showing peaks and valleys at the ...
Functional data can be clustered by plugging estimated regression coefficients from individual curve...
Functional data clustering procedures seek to identify subsets of curves with similar shapes and est...
Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has...
ABSTRACT. Data in many different fields come to practitioners through a process naturally described ...
In this paper we present a family of models and learning algorithms that can simultaneously align ...
In recent years curve clustering problem has been handled in several applicative fields. However, mo...
We develop a new method to locally cluster curves and discover functional motifs, i.e.~typical ``sha...
Abstract In this paper, we deal with the problem of curves clustering. We propose a nonparametric me...
MICA enables the automatic synchronization of discrete data curves. To this end, characteristic poin...
The problem of curve clustering when curves are misaligned is considered. A novel algorithm is descr...
A problem, often encountered in functional data analysis, is misalignment of the data. Many methods ...
We consider the issue of classification of functional data and, in particular, we deal with the prob...
The problem of detecting clusters is a common issue in the analysis of functional data and some int...
In this paper we present a family of algorithms that can simultaneously align and cluster sets of mu...
Functional data that are not perfectly aligned in the sense of not showing peaks and valleys at the ...
Functional data can be clustered by plugging estimated regression coefficients from individual curve...
Functional data clustering procedures seek to identify subsets of curves with similar shapes and est...
Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has...
ABSTRACT. Data in many different fields come to practitioners through a process naturally described ...
In this paper we present a family of models and learning algorithms that can simultaneously align ...
In recent years curve clustering problem has been handled in several applicative fields. However, mo...
We develop a new method to locally cluster curves and discover functional motifs, i.e.~typical ``sha...
Abstract In this paper, we deal with the problem of curves clustering. We propose a nonparametric me...
MICA enables the automatic synchronization of discrete data curves. To this end, characteristic poin...