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Feature selection for high dimensional data.
Year: 2008  
Authors: A. Destrero, S. Mosci, C. De Mol, A. Verri, F. Odone  
Journal: Computational Management Science Volume: 6
Pages: 25-40
This paper focuses on feature selection for problems dealing with high-dimensional data. We discuss the benefits of adopting a regularized approach with L1 or L1–L2 penalties in two different applications—microarray data analysis in computational biology and object detection in computer vision. We describe general algorithmic aspects as well as architecture issues specific to the two domains. The very promising results obtained show how the proposed approach can be useful in quite different fields of application.
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