I have a dataset which consists of around 46k observations and 20k features. The target vector is of length 75 (and so the target matrix is 46k x 75). Among the features few are categorical and others are numerical. What are the possible best approaches for feature selection for these kind of datasets?
I am aware that unsupervised techniques such as PCA could be applied here, but what other approaches could be applied. Many variables with 0 variance were initially removed.