An Efficient Algorithm for Large Scale Compressive Feature Learning.

This paper focuses on large-scale unsupervised feature selection from text. We expand upon the recently proposed Compressive Feature Learning (CFL) framework, a method that uses dictionary-based compression to select a K-gram representation for a document corpus. We show that CFL is NP-Complete and provide a novel and efficient approximation algorithm based on a homotopy that transforms a convex relaxation of CFL into the original problem. Our algorithm allows CFL to scale to corpuses comprised of millions of documents because each step is linear in the corpus length and highly parallelizable. We use it to extract features from the BeerAdvocate dataset, a corpus of over 1.5 million beer reviews spanning 10 years. CFL uses two orders of magnitude fewer features than the full trigram space. It beats a standard unigram model in a number of prediction tasks and achieves nearly twice the accuracy on an author identification task.