High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.Comment: 18 page

Characterization of Additive Manufactured Scaffolds

At the increasing pace with which additive manufacturing technologies are advancing, it is possible nowadays to fabricate a variety of three-dimensional (3D) scaffolds with controlled structural and architectural properties. Examples span from metal cellular solids, which find application as prosthetic devices, to bioprinted constructs holding the promise to regenerate tissues and organs. These 3D porous constructs can display a variety of physicochemical and mechanical properties depending on the used material and on the design of the pore network to be created. To determine how these properties change with changing the scaffold’s design criteria, a plethora of characterization methods are applied in the biofabrication field. In this chapter, we review the most common techniques used to characterize such fabricated scaffolds by additive manufacturing technologies