Abstract / truncated to 115 words (read the full abstract)

In order to find more sophisticated trends in data, potential correlations between larger and larger groups of variables must be considered. Unfortunately, the number of such correlations generally increases exponentially with the number of input variables and, as a result, brute force approaches become unfeasible. So, the data needs to be simplified sufficiently. Yet, the data may not be oversimplified. A method that is widely used for this purpose is to first cast the data as a matrix and the compute a low rank matrix approximation. The tight equivalences between the Weighted Low Rank Approximation (WLRA) problem and the Total Least Squares (TLS) problem are explored. Despite the seemingly different problem formulations of WLRA and ... toggle 1 keyword

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