A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Mixtures of probabilistic principal component analyzers
Neural Computation
Generalized feature extraction for structural pattern recognition in time-series data
Generalized feature extraction for structural pattern recognition in time-series data
Initializing EM using the properties of its trajectories in Gaussian mixtures
Statistics and Computing
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Introduction to Clustering Large and High-Dimensional Data
Introduction to Clustering Large and High-Dimensional Data
Crisp and fuzzy k-means clustering algorithms for multivariate functional data
Computational Statistics
PLS classification of functional data
Computational Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based clustering for multivariate functional data
Computational Statistics & Data Analysis
| Hi-index | 0.01 |
A new method for clustering functional data is proposed under the name Funclust. This method relies on the approximation of the notion of probability density for functional random variables, which generally does not exist. Using the Karhunen-Loeve expansion of a stochastic process, this approximation leads to define an approximation for the density of functional variables. Based on this density approximation, a parametric mixture model is proposed. The parameter estimation is carried out by an EM-like algorithm, and the maximum a posteriori rule provides the clusters. The efficiency of Funclust is illustrated on several real datasets, as well as for the characterization of the Mars surface.