Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component. Existing methods that tackle this task use the nuclear norm and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\ell _1$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>ℓ</mi> <mn>1</mn> </msub> </math> </EquationSource> </InlineEquation>-cost functions as convex relaxations of the rank constraint and the sparsity measure,...</equationsource></equationsource></inlineequation>

Tree-based methods are statistical procedures for automatic learning from data, whose main applications are integrated into a data-mining environment for decision support systems. Here, we focus on two problems of decision trees: the stability of the rules obtained and their applicability to...