Learning Mixture Model with Missing Values and its Application to Rankings
Learning Mixture Model with Missing Values and its Application to Rankings
复制标题
具有缺失值的学习混合模型及其在排名中的应用
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Dogyoon Song
中科院分区：
文献类型：
--
作者：
Devavrat Shah;Dogyoon Song
我们考虑基于缺失值的观察结果，学习通用下高斯分布的混合物的问题。为此，我们利用了文献（软性硬性值阈值）的矩阵估计方法。具体而言，我们堆叠观测值（缺少值）以形成数据矩阵并了解其低级别的近似值，以便可以使用简单的基于距离的算法正确地将行索引群集群集属于适当的混合物组件。为了通过量化有限样本结合来分析该算法的性能，我们以两种重要方式扩展了文献中矩阵估计方法的结果：一种，跨列之间的噪声是相关的，并且在文献中所考虑的所有矩阵所有条目中都不独立。第二，感兴趣的性能指标是最大L2行规范误差，它比在所有条目上平均的传统于点误差要强。在矩阵估计的背景下，我们能够在缺少数据的存在下连接矩阵估计和混合模型学习。
We consider the question of learning mixtures of generic sub-gaussian distributions based on observations with missing values. To that end, we utilize a matrix estimation method from literature (softor hardsingular value thresholding). Specifically, we stack the observations (with missing values) to form a data matrix and learn a low-rank approximation of it so that the row indices can be correctly clustered to belong to appropriate mixture component using a simple distance-based algorithm. To analyze the performance of this algorithm by quantifying finite sample bound, we extend the result for matrix estimation methods in the literature in two important ways: one, noise across columns is correlated and not independent across all entries of matrix as considered in the literature; two, the performance metric of interest is the maximum l2 row norm error, which is stronger than the traditional mean-squared-error averaged over all entries. Equipped with these advances in the context of matrix estimation, we are able to connect matrix estimation and mixture model learning in the presence of missing data.
DOI：
--
发表时间：
2016-09
期刊：
ArXiv
影响因子：
--
作者：
C. Daskalakis;Christos Tzamos;Manolis Zampetakis
通讯作者：
C. Daskalakis;Christos Tzamos;Manolis Zampetakis
关于学习分离良好的高斯的混合
DOI：
10.1109/focs.2017.17
发表时间：
2017
期刊：
Proceedings of 58th Annual IEEE Symposium on the Foundations of Computer Science
影响因子：
--
作者：
Regev, Oded;Vijayaraghavan, Aravindan
通讯作者：
Vijayaraghavan, Aravindan