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 ...

Notice that reconstruction uses eigenvectors as rows rather than columns. Once again, the eigenvectors columns-vs-rows issue can be tricky when working with PCA. Next, the original source data is ...