Ranky : An Approach to Solve Distributed SVD on Large Sparse Matrices

TL;DR

Ranky introduces a distributed method to accurately compute SVD of large, sparse matrices, effectively addressing rank-related challenges with minimal error, applicable in various data-intensive fields.

Contribution

The paper presents Ranky, a novel set of methods specifically designed to solve the rank problem in distributed SVD computations for large sparse matrices.

Findings

Successfully recovers singular values and vectors with negligible error.

Addresses rank issues in distributed SVD algorithms.

Applicable to large-scale sparse data matrices.

Abstract

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.