Introduction to algorithms
Control of selective perception using Bayes nets and decision theory
International Journal of Computer Vision - Special issue on active vision II
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
The budgeted maximum coverage problem
Information Processing Letters
A combinatorial strongly polynomial algorithm for minimizing submodular functions
Journal of the ACM (JACM)
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Proceedings of the 5th international conference on Multimodal interfaces
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Sensor management using an active sensing approach
Signal Processing
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Gait feature subset selection by mutual information
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
S-SEER: selective perception in a multimodal office activity recognition system
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Discrimination gain to optimize detection and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Novel Approach for Optimal Cost-Effective Design of Complex Repairable Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bottom-Up Construction of Minimum-Cost and/ or Trees for Sequential Fault Diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A note on maximizing a submodular set function subject to a knapsack constraint
Operations Research Letters
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As sensors become more complex and prevalent, they present their own issues of cost effectiveness and timeliness. It becomes increasingly important to select sensor sets that provide the most information at the least cost and in the most timely and efficient manner. Two typical sensor selection problems appear in a wide range of applications. The first type involves selecting a sensor set that provides the maximum information gain within a budget limit. The other type involves selecting a sensor set that optimizes the tradeoff between information gain and cost. Unfortunately, both require extensive computations due to the exponential search space of sensor subsets. This paper proposes efficient sensor selection algorithms for solving both of these sensor selection problems. The relationships between the sensors and the hypotheses that the sensors aim to assess are modeled with Bayesian networks, and the information gain (benefit) of the sensors with respect to the hypotheses is evaluated by mutual information. We first prove that mutual information is a submodular function in a relaxed condition, which provides theoretical support for the proposed algorithms. For the budget-limit case, we introduce a greedy algorithm that has a constant factor of (1 - 1/e) guarantee to the optimal performance. A partitioning procedure is proposed to improve the computational efficiency of the algorithms by efficiently computing mutual information as well as reducing the search space. For the optimal-tradeoff case, a submodular-supermodular procedure is exploited in the proposed algorithm to choose the sensor set that achieves the optimal tradeoff between the benefit and cost in a polynomial-time complexity.