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Sparse Algorithms are Not Stable: A No-free-lunch Theorem
This video was recorded at NIPS Workshops, Lake Tahoe 2012. We consider two widely used notions in machine learning, namely: sparsity and stability. Both notions are deemed desirable, and are believed to lead to good generalization ability. We show that these two notions contradict each other: a sparse algorithm can not be stable and vice versa. Thus, one has to tradeoff sparsity and stability in designing a learning algorithm. This implies that, in contrast to \ell_2 regularized regression, \ell_1 regularized regression (Lasso) cannot be stable.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
