Model-based clustering of high-dimensional data: A review
Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …
foundations and its flexibility. However, high-dimensional data are nowadays more and …
Dynamically exploiting narrow width operands to improve processor power and performance
In general-purpose microprocessors, recent trends have pushed towards 64 bit word widths,
primarily to accommodate the large addressing needs of some programs. Many integer …
primarily to accommodate the large addressing needs of some programs. Many integer …
Early fault diagnosis and classification of ball bearing using enhanced kurtogram and Gaussian mixture model
Y Hong, M Kim, H Lee, JJ Park… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Ball bearing failure is one of the major obstacles to the effective operation of large
mechanical systems. During maintenance, the initial diagnosis of a fault within the bearing is …
mechanical systems. During maintenance, the initial diagnosis of a fault within the bearing is …
Challenges in model‐based clustering
V Melnykov - Wiley interdisciplinary reviews: computational …, 2013 - Wiley Online Library
Abstract Model‐based clustering is an increasingly popular area of cluster analysis that
relies on probabilistic description of data by means of finite mixture models. Mixture …
relies on probabilistic description of data by means of finite mixture models. Mixture …
Examining the effect of initialization strategies on the performance of Gaussian mixture modeling
Mixture modeling is a popular technique for identifying unobserved subpopulations (eg,
components) within a data set, with Gaussian (normal) mixture modeling being the form …
components) within a data set, with Gaussian (normal) mixture modeling being the form …
Improved initialization of the em algorithm for mixture model parameter estimation
B Panić, J Klemenc, M Nagode - Mathematics, 2020 - mdpi.com
A commonly used tool for estimating the parameters of a mixture model is the Expectation–
Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum …
Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum …
Improved initialisation of model-based clustering using Gaussian hierarchical partitions
Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting
points in the parameter space often lead to different local maxima of the likelihood function …
points in the parameter space often lead to different local maxima of the likelihood function …
[HTML][HTML] Extending mixtures of factor models using the restricted multivariate skew-normal distribution
The mixture of factor analyzers (MFA) model provides a powerful tool for analyzing high-
dimensional data as it can reduce the number of free parameters through its factor-analytic …
dimensional data as it can reduce the number of free parameters through its factor-analytic …
Initialization of hidden Markov and semi‐Markov models: A critical evaluation of several strategies
The expectation–maximization (EM) algorithm is a familiar tool for computing the maximum
likelihood estimate of the parameters in hidden Markov and semi‐Markov models. This …
likelihood estimate of the parameters in hidden Markov and semi‐Markov models. This …
Flexible mixture modelling using the multivariate skew-t-normal distribution
TI Lin, HJ Ho, CR Lee - Statistics and Computing, 2014 - Springer
This paper presents a robust probabilistic mixture model based on the multivariate skew-t-
normal distribution, a skew extension of the multivariate Student'st distribution with more …
normal distribution, a skew extension of the multivariate Student'st distribution with more …