High-dimensional model recovery from random sketched data by exploring intrinsic sparsity

Learning from large-scale and high-dimensional data still remains a computationally challenging problem, though it has received increasing interest recently. To address this issue, randomized reduction methods have been developed by either reducing the dimensionality or reducing the number of training instances to obtain a small sketch of the original data. In this paper, we focus on recovering a high-dimensional classification/regression model from random sketched data. We propose to exploit the intrinsic sparsity of optimal solutions and develop novel methods by increasing the regularization parameter before the sparse regularizer. In particular, (i) for high-dimensional classification problems, we leverage randomized reduction methods to reduce the dimensionality of data and solve a dual formulation on the random sketched data with an introduced sparse regularizer on the dual solution; (ii) for high-dimensional sparse least-squares regression problems, we employ randomized reduction methods to reduce the scale of data and solve a formulation on the random sketched data with an increased regularization parameter before the sparse regularizer. For both classes of problems, by exploiting the intrinsic sparsity of the optimal dual solution or the optimal primal solution we provide formal theoretical guarantee on the recovery error of learned models in comparison with the optimal models that are learned from the original data. Compared with previous studies on randomized reduction for machine learning, the present work enjoy several advantages: (i) the proposed formulations enjoys intuitive geometric explanations; (ii) the theoretical guarantee does not rely on any stringent assumptions about the original data (e.g., low-rankness of the data matrix or the data are linearly separable); (iii) the theory covers both smooth and non-smooth loss functions for classification; (iv) the analysis is applicable to a broad class of randomized reduction methods as long as the reduction matrices admit the Johnson–Lindenstrauss type of lemma. We also present empirical studies to support the proposed methods and the presented theory.