Results 41 to 50 of about 1,352 (208)
Feature Selection for Supervised Learning and Compression
Applied Artificial Intelligence, 2022 Supervised feature selection aims to find the signals that best predict a target variable. Typical approaches use measures of correlation or similarity, as seen in filter methods, or predictive power in learned models, as seen in wrapper methods. In both
Phillip Taylor +4 more
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On Greedy and Submodular Matrices [PDF]
, 2011 We characterize non-negative greedy matrices, i.e., (0,1)-matrices A such that the problem max {c T x|Ax ≤ b, x ≥ 0} can be solved greedily. We identify so-called submodular matrices as a special subclass of greedy matrices. Finally, we extend the notion of greediness to { − 1,0,1}-matrices. We present numerous applications of these concepts.
Faigle, Ulrich +2 more
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Submodularity in Action: From Machine Learning to Signal Processing Applications [PDF]
, 2020 Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of the well-known convexity/concavity properties in the continuous domain.
Gesbert, David +12 more
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Submodular Cost Submodular Cover with an Approximate Oracle
CoRR, 2019 International Conference on Machine Learning ...
Victoria G. Crawford +2 more
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Fractional 0–1 programming and submodularity [PDF]
, 2022 In this note we study multiple-ratio fractional 0–1 programs, a broad class of NP-hard combinatorial optimization problems. In particular, under some relatively mild assumptions we provide a complete characterization of the conditions, which ensure that ...
Prokopyev, Oleg A; https://orcid.org/ +2 more
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MathematicsInland waterway transportation is critical for the movement of hazardous liquid cargoes. To prevent contamination when transporting different types of liquids, certain shipments necessitate tank cleaning at designated stations between tasks. This process
Yanmeng Tao +3 more
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Submodular Dominance and Applications
CoRR, 2022 In submodular optimization we often deal with the expected value of a submodular function $f$ on a distribution $\mathcal{D}$ over sets of elements. In this work we study such submodular expectations for negatively dependent distributions. We introduce a natural notion of negative dependence, which we call Weak Negative Regression (WNR), that ...
Qiu, Frederick, Singla, Sahil
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On Constructing Finite, Finitely Subadditive Outer Measures, and Submodularity
International Journal of Mathematics and Mathematical Sciences, 2008 Given a nonempty abstract set 𝑋, and a covering class 𝒞, and a finite, finitely subadditive outer measure 𝜈, we construct an outer measure 𝜈 and investigate conditions for 𝜈 to be submodular. We then consider several other set functions associated with 𝜈
Charles Traina
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Determinant-Based Fast Greedy Sensor Selection Algorithm
IEEE Access, 2021 In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-
Yuji Saito +7 more
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Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization
IEEE Access, 2021 The problem of selecting an optimal set of sensors estimating a high-dimensional data is considered. Objective functions based on D-, A-, and E-optimality criteria of optimal design are adopted to greedy methods, that maximize the determinant, minimize ...
Kumi Nakai +4 more
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