We present an alternative approach to decompose non-negative tensors, called many-body approximation .

These counts have very high global sensitivity.

The chain structure is also convenient to handle streaming data as we will explain later. We first give a brief introduction to the EP and CEP framework. Step 2. We construct a tilted distribution to combine the true likelihood, Step 3. We project the tilted distribution back to the exponential family, q KL( null p nullq) where q belongs to the exponential family. Step 4. We update the approximation term by's in parallel, and uses damping to avoid divergence. The above computation are very conveniently to implement.

Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.

As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each.