Semi Supervised Spectral Clustering With Structured Sparsity Regularization
Spectral clustering (SC) is one of the most widely used clustering methods. In this letter, we extend the traditional SC with a semi supervised manner. Specifically, with the guidance of small amount of supervisory information, we build a matrix with anti block diagonal appearance, which is further utilized to regularize the product of the low dimensional embedding and its transpose. Technically, we formulate the proposed model as a constrained optimization problem. Then, we relax it as a convex problem, which can be efficiently solved with the global convergence guaranteed via the inexact augmented Lagrangian multiplier method. Experimental results over four real world datasets demonstrate that higher accuracy and normalized mutual information are achieved when compared with state of the art methods.
IEEE Signal Processing Letters