The objective of this work was to use the GGE biplot method to select superior wheat genotypes for adaptability and stability, and to determine grain yield in Sussundenga, Bárué, and Lichinga, in ...

Abstract: Robust principal component analysis (RPCA) is a technique that aims to make principal component analysis (PCA) robust to noise samples. The current modeling approaches of RPCA were proposed ...

Abstract: Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, while still covering the data subspace as much as ...

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.

The ultimate pit may affect other aspects in the life of a mine such as economical, technical, environmental, and social aspects. What makes it even more complex is that most often there are many pits ...

Part III of this series takes a closer look at principal component analysis (PCA). PCA can be very useful for observing your data when the observations you wish to compare are described by many ...