2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-28.6
Paper Title On the Performance-Complexity Tradeoff in Stochastic Greedy Weak Submodular Optimization
Authors Abolfazl Hashemi, Haris Vikalo, Gustavo de Veciana, University of Texas at Austin, United States
SessionMLSP-28: ML and Time Series
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [OTH-BGDT] Big Data
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Weak submodular optimization underpins many problems in signal processing and machine learning. For such problems, under a cardinality constraint, a simple greedy algorithm is guaranteed to find a solution with a value no worse than $1-e^{-\gamma}$ of the optimal. Given the high cost of queries to large-scale signal processing models, the complexity of \textsc{Greedy} becomes prohibitive in modern 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 \textsc{Greedy}. Specifically, we quantify the effect of uniform sampling strategies on the performance through two criteria: (i) the probability of identifying an optimal subset, and (ii) the suboptimality of the solution’s value with respect to the optimal. Building upon this insight, we propose a simple progressive stochastic greedy algorithm, study its approximation guarantees, and consider its applications to dimensionality reduction and feature selection tasks.