Partitioned Tensor Factorizations for Learning Mixed Membership Models
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Abstract
We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensormethods, which require decomposing an 
tensor, to factorizing O (p/k) sub-tensors each of size 
In addition, we address the issue of negative entries in the empirical method of moments based estimators. We provide sufficient conditions under which our approach has provable guarantees. Our approach obtains competitive empirical results on both simulated and real data.
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