Principal Component Analysis (PCA) is widely used in data analysis and machine learning to reduce the dimensionality of a dataset. The goal is to find a set of linearly uncorrelated (orthogonal) ...

Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered, for example, as movement trajectories on the surface of the earth are ...

It emerges that representations using the proposed derivative principal component analysis recover the underlying derivatives more accurately compared to principal component analysis-based approaches ...

The Data Science Lab Anomaly Detection Using Principal Component Analysis (PCA) The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, ...

The Data Science Lab Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C# Transforming a dataset into one with fewer columns is more complicated than it might seem, ...

Researchers at Nanjing University of Science and Technology (NJUST) developed PCA-3DSIM, a mathematically grounded ...

Principal Component (PCA) (A) and ADMIXTURE (B) analysis of Xinjiang populations.

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