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Found 4 papers, 1 papers with code
Fast Parallel Randomized Algorithm for Nonnegative Matrix Factorization with KL Divergence for Large Sparse Datasets
However, minimizing NMF with KL divergence is much more difficult than minimizing NMF with Frobenius norm; and sparse models, sparse representation and fast algorithms for large sparse datasets are still challenges for NMF with KL divergence.
Inference in topic models: sparsity and trade-off
One of the core problems in this field is the posterior inference for individual data instances.
Probable convexity and its application to Correlated Topic Models
Contrary to the existing belief of intractability, we show that this inference problem is concave under certain conditions.
Managing sparsity, time, and quality of inference in topic models
In this article, we introduce a simple framework for inference in probabilistic topic models, denoted by FW.
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