A key task of data science is to identify relevant features linked certain output variables that are supposed be modeled or predicted. To obtain a small but meaningful model, it important find stochastically independent capturing all the information necessary model predict sufficiently. Therefore, we introduce in this work framework detect linear and non-linear dependencies between different fe...

Manifolds are widely used to model non-linearity arising in a range of computer vision applications. This paper treats statistics on manifolds and the loss of accuracy occurring when linearizing the manifold prior to performing statistical operations. Using recent advances in manifold computations, we present a comparison between the non-linear analog of Principal Component Analysis, Principal ...

Narrow genetic variability may lead to genetic vulnerability of field crops against biotic and abiotic stresses which can cause yield reduction. In this study a set of 37 wheat microsatellite markers linked with identified QTLs for salinity tolerance were used for the assessment of genetic diversity for salinity in 30 promising lines of hexaploid bread wheat (Triticum aestivum L.). A total of 4...

This paper presents a new compression algorithm for 3D dynamic mesh sequences based on the local principal component analysis (LPCA). The algorithm clusters the vertices into a number of clusters using the local similarity between the trajectories in a coordinate system that is defined in each cluster, and thus transforms the original vertex coordinates into the local coordinate frame of their ...

We develop the local theory of surfaces immersed in the pseudo-Galilean space, a special type of Cayley-Klein spaces. We define principal, Gaussian, and mean curvatures. By this, the general setting for study of surfaces of constant curvature in the pseudo-Galilean space is provided. We describe surfaces of revolution of constant curvature. We introduce special local coordinates for surfaces of...

Discriminant analysis (DA), particularly Discriminant Coordinates (DC), is broadly applied in the scientific literature and included in many statistical software packages. DC is used to analyze biomedical data, especially for differential diagnosis on the basis of laboratory profiles. Papers handling influence analysis in DA can be found in the literature; however this topic has been scarcely t...

Interactive statistical graphics can be e ectivly used to nd natural groupings in observations. In this paper we want to demonstrate how clustering and classi cation can be done with three approaches based on highly interactive graphical environments: high{dimensional scatterplots as available in XGobi, parallel coordinate plots as available in ExplorN, and linked low{ dimensional views as avai...

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today’s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today’s data by combining automated statistical analytics with a highl...