Each wireless device has its unique fingerprint, which can be utilized for device identification
and intrusion detection. Most existing literature employs supervised learning techniques and …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

To deal with the temporal dependence between range cells in high resolution range profile
(HRRP), dynamic methods, especially recurrent neural network (RNN), have been …

In this paper, we consider the problem of constructing accurate and flexible statistical
representations for count data, which we often confront in many areas such as data mining …

The interest in automatic surveillance and monitoring systems has been growing over the
last years due to increasing demands for security and law enforcement applications …

A large class of problems can be formulated in terms of the clustering process. Mixture
models are an increasingly important tool in statistical pattern recognition and for analyzing …

For most of the non-Gaussian statistical models, the data being modeled represent strongly
structured properties, such as scalar data with bounded support (eg, beta distribution) …

This paper introduces a novel enhancement for unsupervised feature selection based on
generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the …

Foreground segmentation of moving regions in image sequences is a fundamental step in
many vision systems including automated video surveillance, human-machine interface, and …

Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for
proportional positive vectors modeling and unsupervised learning. However, little attention …