Compressed and Privacy-Sensitive Sparse Regression
Compressed and Privacy-Sensitive Sparse Regression
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DOI：
10.1109/tit.2008.2009605
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发表时间：
2009-02-01
影响因子：
2.5
通讯作者：
Wasserman, Larry
中科院分区：
文献类型：
--
作者：
Zhou, Shuheng;Lafferty, John;Wasserman, Larry
最近的研究研究了稀疏性在高维回归和信号重建中的作用，建立了恢复稀疏模型的理论极限。这项工作表明，l(1)-正则化最小二乘回归可以根据高维度的噪声示例准确估计稀疏线性模型。我们研究这个问题的一个变体，其中原始 n 个输入变量通过随机线性变换压缩为 m
Recent research has studied the role of sparsity in high-dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models. This line of work shows that l(1)-regularized least squares regression can accurately estimate a sparse linear model from noisy examples in high dimensions. We study a variant of this problem where the original n input variables are compressed by a random linear transformation to m