Compressed Least Squares Regression revisited


Martin Slawski ;
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1207-1215, 2017.


We revisit compressed least squares (CLS) regression as originally analyzed in Maillard and Munos (2009) and later on in Kaban (2014) with some refinements. Given a set of high-dimensional inputs, CLS applies a random projection and then performs least squares regression based on the projected inputs of lower dimension. This approach can be beneficial with regard to both computation (yielding a smaller least squares problem) and statistical performance (reducing the estimation error). We will argue below that the outcome of previous analysis of the procedure is not meaningful in typical situations, yielding a bound on the prediction error that is inferior to ordinary least squares while requiring the dimension of the projected data to be of the same order as the original dimension. As a fix, we subsequently present a modified analysis with meaningful implications that much better reflects empirical results with simulated and real data.

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