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: 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.

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There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes ...

Abstract: Principal Component Analysis (PCA) is a widely used technique in process monitoring, fault diagnosis, and soft sensing of industrial systems. Despite its popularity, PCA suffers from the ...