Factorizations for data analysis
Colloquium Speaker: Anna Seigal, Harvard University Title: Factorizations for data analysis Abstract: We can find structure in data by factoring it into building blocks, which should be interpretable for the context at hand. A classical example is principal component analysis (PCA), which uses the eigendecomposition of the covariance matrix to find axes of variation in a dataset. Starting from […]