  Spectral inference provides fast algorithms and provable optimality for
latent topic analysis. But for real data these algorithms require additional
ad-hoc heuristics, and even then often produce unusable results. We explain
this poor performance by casting the problem of topic inference in the
framework of Joint Stochastic Matrix Factorization (JSMF) and showing that
previous methods violate the theoretical conditions necessary for a good
solution to exist. We then propose a novel rectification method that learns
high quality topics and their interactions even on small, noisy data. This
method achieves results comparable to probabilistic techniques in several
domains while maintaining scalability and provable optimality.

Machine Learning;Artificial Intelligence