论文标题
Ranky：在大型稀疏矩阵上求解分布式SVD的方法
Ranky : An Approach to Solve Distributed SVD on Large Sparse Matrices
论文作者
论文摘要
单数值分解（SVD）是许多领域的研究主题，从数据挖掘到图像处理。这些应用程序产生的数据可以表示为矩阵，在该矩阵中，它较大且稀疏。大多数现有的算法用于计算大量矩阵的左右单数矢量的单数值，但不是大且稀疏的矩阵。即使他们可以找到大矩阵的SVD，由于顺序算法，大密度基质的计算具有很高的时间复杂性。提出了用于计算大型矩阵SVD的分布式方法。但是，在使用这些分布式算法求解SVD时，矩阵的等级仍然是一个问题。在本文中，我们提出了Ranky，以分布式方式解决大型和稀疏矩阵上排名问题的方法集。实验结果表明，Ranky方法恢复了奇异值，奇异的左右向量的给定大而稀疏的矩阵，误差可忽略不计。
Singular Value Decomposition (SVD) is a well studied research topic in many fields and applications from data mining to image processing. Data arising from these applications can be represented as a matrix where it is large and sparse. Most existing algorithms are used to calculate singular values, left and right singular vectors of a large-dense matrix but not large and sparse matrix. Even if they can find SVD of a large matrix, calculation of large-dense matrix has high time complexity due to sequential algorithms. Distributed approaches are proposed for computing SVD of large matrices. However, rank of the matrix is still being a problem when solving SVD with these distributed algorithms. In this paper we propose Ranky, set of methods to solve rank problem on large and sparse matrices in a distributed manner. Experimental results show that the Ranky approach recovers singular values, singular left and right vectors of a given large and sparse matrix with negligible error.
