Factorizations for data analysis
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesColloquium 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 […]