Bounded Projection Matrix Approximation with Applications to Community
  Detection
Zheng Zhai Hengchao Chen Qiang Sun
Community detection is an important problem in unsupervised learning. This
paper proposes to solve a projection matrix approximation problem with an
additional entrywise bounded constraint. Algorithmically, we introduce a new
differentiable convex penalty and derive an alternating direction method of
multipliers (ADMM) algorithm. Theoretically, we establish the convergence
properties of the proposed algorithm. Numerical experiments demonstrate the
superiority of our algorithm over its competitors, such as the semi-definite
relaxation method and spectral clustering.