A method for principal component analysis is proposed that is sparse and robust at the same
time. The sparsity delivers principal components that have loadings on a small number of …

The inverse square root of a covariance matrix is often desirable for performing data
whitening in the process of applying many common multivariate data analysis methods …

Principal component analysis is a popular data analysis dimensionality reduction technique,
aiming to project with minimum error for a given dataset into a subspace of smaller number …

We propose a new technique combining dynamic and static analysis of programs to find
linear invariants. We use a statistical tool, called simple component analysis, to analyze …

We present the tool Random (R-based Analyzer for Numerical DOMains) for static analysis
of imperative programs. The tool is based on the theory of abstract interpretation and …

A Tool Which Mines Partial Execution Traces to Improve Static Analysis Page 1 A Tool Which
Mines Partial Execution Traces to Improve Static Analysis Gianluca Amato, Maurizio Parton …

[ES] Una de las áreas más importantes de la investigación actual en el análisis de datos
multivariantes se centra en el desarrollo de técnicas eficientes para el estudio matrices de …

The need to effectively analyse high dimensional data is increasingly crucial to many fields
as data collection and storage capabilities continue to grow. Working with high dimensional …

Interpretation of principal components is difficult due to their weights (loadings, coefficients)
being of various sizes. Whereas very small weights or very large weights can give clear …

Student past and present performances are analyzed, compared, and reflected upon by
teachers, curriculum developers, and educational researchers. For the tasks, methods and …