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We propose a new two stage algorithm LING for large scale regressionproblems. LING has the same risk as the well known Ridge Regression under thefixed design setting and can be computed much faster. Our experiments haveshown that LING performs well in terms of both prediction accuracy andcomputational efficiency compared with other large scale regression algorithmslike Gradient Descent, Stochastic Gradient Descent and Principal ComponentRegression on both simulated and real datasets.

Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent