2019/05/23 by Abolfazl Hashemi, Hashemi, Abolfazl, Mahsa Ghasemi +5
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Machine Learning and Algorithms #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1905.09919
To be published in proceedings of International Conference on Machine Learning (ICML) 2019
arxiv created 2019/05/23 · openalex publication_date 2019/05/23 · arxiv updated 2019/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the problem of selecting most informative subset of a large observation set to enable accurate estimation of unknown parameters. This problem arises in a variety of settings in machine learning and signal processing including feature selection, phase retrieval, and target localization. Since for quadratic measurement models the moment matrix of the optimal estimator is generally unknown, majority of prior work resorts to approximation techniques such as linearization of the observation model to optimize the alphabetical optimality criteria of an approximate moment matrix. Conversely, by exploiting a connection to the classical Van Trees' inequality, we derive new alphabetical optimality criteria without distorting the relational structure of the observation model. We further show that under certain conditions on parameters of the problem these optimality criteria are monotone and (weak) submodular set functions. These results enable us to develop an efficient greedy observation selection algorithm uniquely tailored for quadratic models, and provide theoretical bounds on its achievable utility.