High-dimensional data and high-dimensional representations of reality are inherent features
of modern Artificial Intelligence systems and applications of machine learning. The well …

Data clustering is an unsupervised data analysis and data mining technique, which offers
refined and more abstract views to the inherent structure of a data set by partitioning it into a …

With the introductory paragraph, we explain the fundamental concepts and basic notions of
this section. For you, the analyst, who has the difficult task to deal with measurements …

This paper is concerned with personal identification using a robust EigenECG network
(REECGNet) based on time-frequency representations of electrocardiogram (ECG) signals …

The computation of robust regression estimates often relies on minimization of a convex
functional on a convex set. In this paper we discuss a general technique for a large class of …

The article reports the results of a comparative study of two robust Principal Component
Analysis (PCA) algorithms based on Projection Pursuit which can be used for exploratory …

This work is devoted to the development of scalable and robust algorithms for data mining
and knowledge discovery problems. The main interest lies in so-called prototype-based …

We consider the affine equivariant sign covariance matrix (SCM) introduced by Visuri et
al.(J. Statist. Plann. Inference 91 (2000) 557). The population SCM is shown to be …

Even in one dimension the sample median exhibits very poor performance when used in
conjunction with the bootstrap. For example, both the percentile‐t bootstrap and the …

The rapid advancement in neurotechnology in recent years has created an emerging critical
intersection between neurotechnology and security. Implantable devices, non-invasive …