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Showing 1 to 4 of 4 for “"Alternating Least Squares (ALS)"”.

  1. Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization

    … matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) are widely used in the physical sciences; this thesis explores the often-overlooked origins of NMF in the psychometrics literature. Another method originating in psychometrics is sequentially-fit factor …

    uiuc Repository record for Sequentially-fit alternating least squares algorithms in nonnegative matrix factorization (opens in a new tab)

  2. CONVERGENGE ANALYSIS ON SVD-BASED ALGORITHMS FOR TENSOR LOW RANK APPROXIMATIONS

    … to the conventional approach by the so-called alternating least squares (ALS) method that works to adjust one factor a time, proposed SVD-based algorithms improve two factors simultaneously. Convergence analysis both for the generalized Rayleigh quotient and the iterates themselves is the main …

    nus Repository record for CONVERGENGE ANALYSIS ON SVD-BASED ALGORITHMS FOR TENSOR LOW RANK APPROXIMATIONS (opens in a new tab)

  3. Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems

    … ratings deviate from average true ratings. We also reduce these unfairness in matrix factorization (MF) models by explicitly adding them as penalty terms to learning objectives. Next, we target a form of unfairness in matrix factorization models observed as disparate model performance across …

    vt Repository record for Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems (opens in a new tab)

  4. Algorithms and software for efficient tensor decompositions

    … for real-world applications. We introduce a new alternating optimization algorithm that achieves theoretically guaranteed superlinear local convergence for exact CP rank tensors. By leveraging a Mahalanobis norm formulation, we propose a method that interpolates between classical Alternating

    uiuc Repository record for Algorithms and software for efficient tensor decompositions (opens in a new tab)