  In recent years, spectral clustering has become a standard method for data
analysis used in a broad range of applications. In this paper we propose a new
class of algorithms for multiway spectral clustering based on optimization of a
certain "contrast function" over the unit sphere. These algorithms, partly
inspired by certain Independent Component Analysis techniques, are simple, easy
to implement and efficient.
  Geometrically, the proposed algorithms can be interpreted as hidden basis
recovery by means of function optimization. We give a complete characterization
of the contrast functions admissible for provable basis recovery. We show how
these conditions can be interpreted as a "hidden convexity" of our optimization
problem on the sphere; interestingly, we use efficient convex maximization
rather than the more common convex minimization. We also show encouraging
experimental results on real and simulated data.

Machine Learning;Machine Learning