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) ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Principal component analysis (PCA) is a commonly used statistical method in a wide range of applications. However, it does not work well when the number of features is larger than the sample size. We ...

We propose two methods based on the functional principal component analysis (FPCA) to estimate smooth derivatives for a sample of observed curves with a multidimensional domain. We apply the ...