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Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms
Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms
Abstract. Matrix/tensor factorization models such as principal component analysis , nonnegative matrix factorization, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction, which are important in analyzing high-dimensional data that comes in large volume. Their diverse applications include image denoising and reconstruction, dictionary learning, topic modeling, and network data analysis. …