Ranky : An Approach to Solve Distributed SVD on Large Sparse Matrices
This addresses a computational bottleneck for applications like data mining and image processing that use large sparse matrices, though it appears incremental as it builds on distributed SVD methods.
The authors tackled the problem of computing Singular Value Decomposition (SVD) for large sparse matrices in a distributed setting, where existing methods struggle with rank issues, and their Ranky approach achieved recovery with negligible error.
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.