Fast learning of scale‐free networks based on Cholesky factorization
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DOI: 10.1002/int.21984
Abstract: Recovering network connectivity structure from high‐dimensional observations is of increasing importance in statistical learning applications. A prominent approach is to learn a Sparse Gaussian Markov Random Field by optimizing regularized maximum likelihood, where the sparsity… read more here.
Keywords: scale free; free networks; learning scale; fast learning ... See more keywords