We aim to learn sentence representations from as many unlabeled queries as possible via principal component analysis (PCA): specifically, learn a projection matrix for embedding a bag-of-words vector into a lowdimensional dense feature vector.
pCA has been a workhorse in representation learning, e.g., inducing features for face recognition (Turk et al., 1991).