A Sparse SVD Method for High-dimensional Data
A Sparse SVD Method for High-dimensional Data
复制标题
一种高维数据的稀疏SVD方法
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Buja
中科院分区：
文献类型：
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作者：
Dan Yang;Zongming Ma;A. Buja
我们提出了一种新的计算方法来近似一个大的，嘈杂的数据表的低秩矩阵稀疏奇异向量。近似值是从同时产生奇异向量的阈值子空间迭代中获得的，而不是像竞争提案中那样连续地产生奇异向量。我们引入了新的方法来估计阈值参数，从而避免了计算上昂贵的交叉验证的需要。我们还介绍了一种方法来稀疏初始化算法的计算节省，使我们的算法优于香草SVD的完整的数据表时，信号是稀疏的。与现有的两个稀疏SVD方法的比较表明，我们的算法是计算总是更快，统计上总是至少可比的两个竞争的算法。
We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximation is obtained from thresholded subspace iterations that produce the singular vectors simultaneously, rather than successively as in competing proposals. We introduce novel ways to estimate thresholding parameters which obviate the need for computationally expensive cross-validation. We also introduce a way to sparsely initialize the algorithm for computational savings that allow our algorithm to outperform the vanilla SVD on the full data table when the signal is sparse. A comparison with two existing sparse SVD methods suggests that our algorithm is computationally always faster and statistically always at least comparable to the better of the two competing algorithms.
影响因子：
2.8
作者：
Green, JC;Kivelson, MG
通讯作者：
Kivelson, MG
影响因子：
2.1
作者：
Witten, Daniela M.;Tibshirani, Robert;Hastie, Trevor
通讯作者：
Hastie, Trevor