Introduces a core-conditioned regularized tri-factorization framework for low-rank approximation that jointly manages accuracy, factor scale, and numerical conditioning with supporting analysis and validation.
K., ”A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data,” SIAM Journal on Mathematics of Data Science, vol
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Core-Conditioned Regularized Matrix Tri-Factorization for High-Dimensional Structured Systems
Introduces a core-conditioned regularized tri-factorization framework for low-rank approximation that jointly manages accuracy, factor scale, and numerical conditioning with supporting analysis and validation.