Submodular Dictionary Selection for Sparse Representation

By Andreas Krause, Volkan Cevher. 2010

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Citations in the corpus

Learning with Submodular Functions: A Convex Optimization Perspective (2011, background): “Among these set-functions, submodular functions play an important role, similar to convex functions on vector spaces, as many functions that occur in practical problems turn out to be submodular functions or slight modifications thereof, with applications in many areas areas of computer science and ”
Cross-Modality Submodular Dictionary Learning for Information Retrieval (2014, methodology): “Submodularity: Submodulairty has recently been applied to manycomputervisiontasks, includingclustering [ 19 ] , dictionary selection [ 16 ] and segmentation [ 13 ] .”
Fast Parallel Algorithms for Feature Selection (2019, methodology): “This uses the traditional greedy algorithm to select elements with the largest marginal contribution at each round Krause & Cevher (2010).”
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection (2011, background): “The part of a variable Z that is not correlated with the X i for all i ∈ S , i.e., the part that cannot be explained by the X i , is called the residual (see [3]), and deﬁned as Res( Z, S ) = Z − i S α i X i .”
Guarantees for Greedy Maximization of Non-submodular Functions with Applications (2017, methodology): “5) and Krause et al. (2008). To make this work self-contained, we present the details here.”
Restricted Strong Convexity Implies Weak Submodularity (2016, background): “Krause and Cevher [28] draw similar connections between submodularity and sparse regression, but they require a much stronger coherence-based assumption.”