  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.
