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.
and Bouman, C., ”Covariance Estimation for High Dimension al Data Vectors Using the Sparse Matrix Transform,” in Advances in Neural Information Processing S ystems, 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.