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Sparse Algorithms Are Not Stable: A No-Free-Lunch Theorem.
- Source :
-
IEEE Transactions on Pattern Analysis & Machine Intelligence . Jan2012, Vol. 34 Issue 1, p187-193. 0p. - Publication Year :
- 2012
-
Abstract
- We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We show that these two properties are fundamentally at odds with each other: A sparse algorithm cannot be stable and vice versa. Thus, one has to trade off sparsity and stability in designing a learning algorithm. In particular, our general result implies that ℓ₁-regularized regression (Lasso) cannot be stable, while ℓ₂-regularized regression is known to have strong stability properties and is therefore not sparse. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 34
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 67368306
- Full Text :
- https://doi.org/10.1109/TPAMI.2011.177