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Abstract:
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Sample reuse methods like the bootstrap and cross-validation are widely used in statistics and machine learning. They provide measures of accuracy with some face value validity that is not dependent on strong model assumptions. These methods depend on repeating or omitting cases, while keeping all the variables in those cases. But for many data sets, it is not obvious whether the rows are cases and columns are variables, or vice versa. For example, with movie ratings organized by movie and customer, both movie and customer IDs can be thought of as variables. This talk looks at cross-validation methods that treat rows and columns of the matrix symmetrically, leaving out some rows of some columns. The goal is to pick the rank k in a truncated SVD or similar model, such as the nonnegative matrix factorization. Joint work with Patrick Perry.
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