Rethinking LDA: moment matching for discrete ICA
arXiv:1507.01784
Abstract
We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techniques such as joint diagonalization of tensors to improve over existing methods based on the tensor power method. In an extensive set of experiments on both synthetic and real datasets, we show that our new combination of tensors and orthogonal joint diagonalization techniques outperforms existing moment matching methods.
30 pages; added plate diagrams and clarifications, changed style, corrected typos, updated figures. in Proceedings of the 29-th Conference on Neural Information Processing Systems (NIPS), 2015