On the Benefits of Progressively Increasing Sampling Sizes in Stochastic Greedy Weak Submodular Maximization

Many problems in signal processing and machine learning can be formalized as weak submodular optimization tasks. For such problems, a simple greedy algorithm (GREEDY) is guaranteed to find a solution achieving the objective with a value no worse than 1 - e(-1/c) of the optimal, where c is the multiplicative weak-submodularity constant. Due to the high cast of querying large-scale systems, the complexity of GREEDY becomes prohibitive in contemporary applications. In this work, we study the tradeoff between performance and complexity when one resorts to random sampling strategies to reduce the query complexity of GREEDY. Specifically, we quantify the effect of uniform sampling strategies on GREEDY's performance through two metrics: (i) asymptotic probability of identifying an optimal subset, and (ii) suboptimality with respect to the optimal solution. The latter implies that uniform sampling strategies with a fixed sampling size achieve a non-trivial approximation factor; however, we show that with overwhelming probability, these methods fail to find the optimal subset. Our analysis shows that the failure of uniform sampling strategies with fixed sample size can be circumvented by successively increasing the size of the search space. Building upon this insight, we propose a simple progressive stochastic greedy algorithm and study its approximation guarantees. Moreover, we demonstrate effectiveness of the proposed method in dimensionality reduction applications and feature selection tasks for clustering and object tracking.