论文标题
Wasserstein K-均用于聚类层析成像预测
Wasserstein K-Means for Clustering Tomographic Projections
论文作者
论文摘要
由单粒子冷冻电子显微镜（Cryo-EM）中的2D类平均问题进行的，我们基于旋转不变的WASSERSTEIN表示图像的K-Means算法。与基于欧几里得（$ L_2 $）距离的现有方法不同，我们证明Wasserstein Metric可以更好地适应不同粒子视图之间的平面外角差异。我们在合成数据集上证明，与$ L_2 $基线相比，我们的方法给出了优越的结果。此外，由于使用快速线性时近似与Wasserstein-1度量标准（也称为Earthmover的距离）使用了快速线性时间近似，因此几乎没有计算上的开销。
Motivated by the 2D class averaging problem in single-particle cryo-electron microscopy (cryo-EM), we present a k-means algorithm based on a rotationally-invariant Wasserstein metric for images. Unlike existing methods that are based on Euclidean ($L_2$) distances, we prove that the Wasserstein metric better accommodates for the out-of-plane angular differences between different particle views. We demonstrate on a synthetic dataset that our method gives superior results compared to an $L_2$ baseline. Furthermore, there is little computational overhead, thanks to the use of a fast linear-time approximation to the Wasserstein-1 metric, also known as the Earthmover's distance.
