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 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) ...

This course will cover statistical approaches to dimension reduction using principal components and factor analysis, and classification using discriminant functions. The first part will outline the ...

Similarly, for a classical archeological data set with a large spread in shape space, geodesic PCA allows new insights that have not been available under PCA by Euclidean approximation. We conclude by ...