论文标题
对R-Local和K-Sparse未标记感测的初始化分析，交替最小化算法和初始化分析
Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing
论文作者
论文摘要
未标记的传感是带有排序测量值的线性反问题。我们提出了一种交替的最小化（Altmin）算法，适用于两个被广泛考虑的置换模型的初始化：部分洗牌/$ k $ -s-sparse排列和$ r $ $ $ - 局部/块对角线排列。 Altmin算法性能的关键是初始化。对于确切的未标记的传感问题，假设高斯测量矩阵或次高斯信号，我们以块$ s $的数量和随机$ k $的数量来绑定初始化错误。实验结果表明，我们的算法很快，适用于置换模型，并且适用于测量矩阵的选择。我们还在几个真实数据集上测试了链接线性回归问题的算法，并且与基线方法相比显示出卓越的性能。
Unlabeled sensing is a linear inverse problem with permuted measurements. We propose an alternating minimization (AltMin) algorithm with a suitable initialization for two widely considered permutation models: partially shuffled/$k$-sparse permutations and $r$-local/block diagonal permutations. Key to the performance of the AltMin algorithm is the initialization. For the exact unlabeled sensing problem, assuming either a Gaussian measurement matrix or a sub-Gaussian signal, we bound the initialization error in terms of the number of blocks $s$ and the number of shuffles $k$. Experimental results show that our algorithm is fast, applicable to both permutation models, and robust to choice of measurement matrix. We also test our algorithm on several real datasets for the linked linear regression problem and show superior performance compared to baseline methods.
