On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance

Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine learning (ML) methods have shown promising results for predicting compres-sive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of...

Quaternion PCA and sparse PCA for shape variability

Principal component analysis (PCA) is commonly used in the fields of computer graphics and geometry processing for constructing subspaces that represent the variability present in a dataset. Examples of such datasets are configurations of a non-rigid object, poses of a deformable character or snapshots from a simulation. By applying PCA to the...