Principal component analysis is a very complex decomposition that works on data matrices instead of single integer values. In my opinion, PCA is best understood by examining a concrete example, such ...

Using the two principal components of a point cloud for robotic grasping as an example, we will derive a numerical implementation of the PCA, which will help to understand what PCA is and what it does ...

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 ...

Stéphane Dray, Julie Josse, Principal component analysis with missing values: a comparative survey of methods, Plant Ecology, Vol. 216, No. 5, Special Issue: Statistical Analysis of Ecological ...

By using the newly proposed Projected-PCA, the rates of convergence of the smooth factor loading matrices are obtained, which are much faster than those of the conventional factor analysis. The ...

Principal component analysis is a very complex decomposition that works on data matrices instead of single integer values. In my opinion, PCA is best understood by examining a concrete example, such ...