Total Least Squares Regression in Input Sparsity Time

Huaian Diao, Zhao Song, David Woodruff, Xin Yang

Neural Information Processing Systems 

In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices  and B, so that there exists an X satisfying the equation ÂX = B. Typically the problem is overconstrained, meaning that m max(n, d).

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