Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Hongbo Zhou, Qiang Cheng


Regularization technique has become a principle tool for statistics and machine learning research and practice. However, in most situations, these regularization terms are not well interpreted, especially on how they are related to the loss function and data. In this paper, we propose a robust minimax framework to interpret the relationship between data and regularization terms for a large class of loss functions. We show that various regularization terms are essentially corresponding to different distortions to the original data matrix. This minimax framework includes ridge regression, lasso, elastic net, fused lasso, group lasso, local coordinate coding, multiple kernel learning, etc., as special cases. Within this minimax framework, we further gave mathematically exact definition for a novel representation called sparse grouping representation (SGR), and proved sufficient conditions for generating such group level sparsity. Under these sufficient conditions, a large set of consistent regularization terms can be designed. This SGR is essentially different from group lasso in the way of using class or group information, and it outperforms group lasso when there appears group label noise. We also gave out some generalization bounds in a classification setting.