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) ...

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

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, ...

Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation ...

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

Considering the growth, job possibilities, and interest among students, Harvard University is offering free courses on Data Science. Check the list of 9 free courses here.