The performance of the state estimation for Gaussian space models can be degraded if are affected by non-Gaussian process and measurement noises with uncertain degree non-Gaussianity. In this paper, we propose a flexible robust Student's t-based multimodel approach. More specifically, degrees freedom parameter from t-distribution is assumed unknown modelled Markov chain values. order to capture...

Constructing accurate similarity graph is an important process in graph-based clustering. However, traditional methods have three drawbacks, such as the inaccuracy of graph, vulnerability to noise and outliers, need for additional discretization process. In order eliminate these limitations, entropy regularized unsupervised clustering based on maximum correntropy criterion adaptive neighbors (E...