Efficient Regularized Regression withL0Penalty for Variable Selection and Network Construction
نویسندگان
چکیده
منابع مشابه
Efficient Regularized Regression with L0 Penalty for Variable Selection and Network Construction
Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is the L0 regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known that L0 optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (...
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ژورنال
عنوان ژورنال: Computational and Mathematical Methods in Medicine
سال: 2016
ISSN: 1748-670X,1748-6718
DOI: 10.1155/2016/3456153