We present a novel technique based on a multi-resolutional clustering and nonlinear multi-
dimensional scaling of earthquake patterns to investigate observed and synthetic seismic …

An improved manifold learning method, called enhanced semi-supervised local Fisher
discriminant analysis (ESELF), for face recognition is proposed. Motivated by the fact that …

Complex networks play a very important role in various fields of science as data structures,
which aggregate information about mutual relationships between numerous objects. The …

We use modern and novel techniques to study the problems associated with detection and
analysis of multitudinous seismic events, which form the background for isolated great …

Visual and interactive data exploration requires fast and reliable tools for embedding of an
original data space in 3 (2)‐dimensional Euclidean space. Multidimensional scaling (MDS) …

A novel technique based on cluster analysis of the multi-resolutional structure of earthquake
patterns is developed and applied to observed and synthetic seismic catalogs. The …

Abstract This paper describes Graph Investigator, the application intended for analysis of
complex networks. A rich set of application functions is briefly described including graph …

The reduction of the dimensionality is of great interest in the context of big data processing.
Multidimensional scaling methods (MDS) are techniques for dimensionality reduction, where …

In this paper, a novel study of the simulated annealing feature extraction (SAFE) for high-
dimensional remote sensing images is proposed. The approach is based on the greedy …

The embedding of high-dimensional data into 2D/3D space is the most popular way of data
visualization. Despite recent advances in develo** of very accurate dimensionality …