A Planted Clique Perspective on Hypothesis Pruning

Hypothesis pruning is an important prerequisite while working with outlier-contaminated data in many computer vision problems. However, the underlying random structures are barely explored literature, limiting designing efficient algorithms. To this end, we provide a novel graph-theoretic perspective on hypothesis exploiting invariant of data. We introduce planted clique model, central object computational statistics, to investigate information-theoretical and limits problem. In addition, propose inductive learning framework for finding hidden cliques that learns heuristics synthetic graphs generalizes real present competitive experimental results large runtime improvement widely used datasets show its efficacy.