标题： 因子分析用于谱估计 显示英文标题

标题： Factor Analysis for Spectral Estimation

摘要： 功率谱估计是许多应用中的重要工具，例如噪声的白化。流行的多锥方法取得了显著的成功，但对于短信号和样本较少的情况却表现不佳。我们提出了一种统计模型，其中信号是由固定但未知的随机源的随机线性组合给出的。给定多个这样的信号，我们估计这些固定源的功率谱所张成的子空间。将单个功率谱估计投影到这个子空间可以提高估计的准确性。我们为该方法提供了准确性的保证，并在冷冻电子显微镜的模拟和实验数据上进行了验证。 显示英文摘要

摘要： Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.