Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Parsimonious Gaussian mixture models
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
Hi-index | 0.00 |
The Fisher-EM algorithm has been recently proposed in Bouveyron and Brunet (2012) [5] for the simultaneous visualization and clustering of high-dimensional data. It is based on a latent mixture model which fits the data into a latent discriminative subspace with a low intrinsic dimension. Although the Fisher-EM algorithm is based on the EM algorithm, it does not respect at a first glance all conditions of the EM convergence theory. Its convergence toward a maximum of the likelihood is therefore questionable. The aim of this work is twofold. First, the convergence of the Fisher-EM algorithm is studied from the theoretical point of view. In particular, it is proved that the algorithm converges under weak conditions in the general case. Second, the convergence of the Fisher-EM algorithm is considered from the practical point of view. It is shown that the Fisher criterion can be used as a stopping criterion for the algorithm to improve the clustering accuracy. It is also shown that the Fisher-EM algorithm converges faster than both the EM and CEM algorithm.