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

Tugay R., Öğüdücü Ş.

International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah-Alam, Malaysia, 11 - 12 July 2018 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/icscee.2018.8538381
  • City: Shah-Alam
  • Country: Malaysia
  • Istanbul Technical University Affiliated: Yes


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 this matrix is large and sparse. Most existing algorithms are used to calculate singular values, left and right singular vectors of a largedense matrix but not large-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-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-sparse matrix with negligible error.