As an important extension to conventional clustering algorithms

cluster ensemble techniques became a hotspot in machine learning area.In this paper

cluster ensemble problem was first viewed as a direct problem of seeking the best subspace.And then

we formally described the problem as an optimization problem with constraint according to linear algebra

and further transformed into a matrix low rank approximation problem by relaxing the discrete constraint.Lastly

a set of orthonormal basis of the best subspace was attained by solving the singular value decomposition problem of the hypergraph's weighted adjacent matrix.Hereby

a matrix low rank approximation-based algorithm was proposed

which called K-means algorithm to cluster objects according to their coordinates in the low dimensional space and obtained the final clustering result.Experiments on baseline datasets demonstrate the effectiveness of the proposed algorithm

and it outperforms other baseline algorithms.