论文标题
通过素描从模拟中得出的光谱估算
Spectral estimation from simulations via sketching
论文作者
论文摘要
素描是一种随机降低方法，可保留数据的几何结构，并在高维回归，低等级近似和图形稀疏中具有应用。在这项工作中，我们表明素描可用于压缩仿真数据，并且仍然准确估计时间自相关和功率频谱密度。对于给定的压缩比，准确性比使用先前已知的方法要高得多。除了提供理论保证外，我们还将草图应用于甲醇的分子动力学模拟，并发现仅使用10％的数据，光谱密度的估计值是90％精确的。
Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and graph sparsification. In this work, we show that sketching can be used to compress simulation data and still accurately estimate time autocorrelation and power spectral density. For a given compression ratio, the accuracy is much higher than using previously known methods. In addition to providing theoretical guarantees, we apply sketching to a molecular dynamics simulation of methanol and find that the estimate of spectral density is 90% accurate using only 10% of the data.
