Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems

author: Yash Deshpande, Department of Electrical Engineering, Stanford University
published: Aug. 20, 2015,   recorded: July 2015,   views: 43


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Given a large data matrix $A\in\reals^{n\times n}$, we consider the problem of determining whether its entries are i.i.d. with some known marginal distribution $A_{ij}\sim P_0$, or instead $A$ contains a principal submatrix $A_{\sC,\sC}$ whose entries have marginal distribution $A_{ij}\sim P_1\neq P_0$. As a special case, the hidden (or planted) clique problem is finding a planted clique in an otherwise uniformly random graph. Assuming unbounded computational resources, this hypothesis testing problem is statistically solvable provided $|\sC|\ge C \log n$ for a suitable constant $C$. However, despite substantial effort, no polynomial time algorithm is known that succeeds with high probability when $|\sC| = o(\sqrt{n})$. Recently, \cite{meka2013association} proposed a method to establish lower bounds for the hidden clique problem within the Sum of Squares (SOS) semidefinite hierarchy. Here we consider the degree-$4$ SOS relaxation, and study the construction of \cite{meka2013association} to prove that SOS fails unless $k\ge C\, n^{1/3}/\log n$. An argument presented by \cite{BarakLectureNotes} implies that this lower bound cannot be substantially improved unless the witness construction is changed in the proof. Our proof uses the moment method to bound the spectrum of a certain random association scheme, i.e. a symmetric random matrix whose rows and columns are indexed by the edges of an Erd\"os-Renyi random graph.

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