Approximation Bounds for Sparse Principal Component Analysis
  We produce approximation bounds on a semidefinite programming relaxation for sparse principal component analysis. These bounds control approximation ratios for tractable statistics in hypothesis testing problems where data points are sampled from Gaussian models with a single sparse leading component. 
Alexandre d'Aspremont, Francis Bach, Laurent El Ghaoui
math.OC