Sparse regression using mixed norms

By Matthieu Kowalski. 2009

301 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Multimodal Multipart Learning for Action Recognition in Depth Videos (2015, background): “The L p /L q norm of z regarding ξ is represented by (cid:107) z (cid:107) q,p | ξ and is deﬁned as the L q norms of the elements inside each set of ξ followed by an L p norm of the L q values across the sets; mathematically: in which | ξ i | indicates the cardinality of set ξ i .”
Bayesian group factor analysis with structured sparsity (2016, methodology): “…structure of Λ in GFA models belongs to a general class of structured sparsity methods that has drawn attention recently (Zou and Hastie, 2005; Yuan and Lin, 2006; Jenatton et al., 2011, 2010; Kowalski, 2009; Kowalski and Torrésani, 2009; Zhao et al., 2009; Huang et al., 2011; Jia et al., 2010).”
Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients (2009, unknown): “We refer to the forthcoming paper [14] for a thorough analysis of the latter.”
$\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning (2011, background): “For instance, the signal processing community have considered this kind of penalties either for sparse single or multiple signal approximations [12], [ 28 ].”
Structured Sparsity: from Mixed Norms to Structured Shrinkage (2009, background): “Such a hierarchy can be exploited by a mixed norm penalty [ 7 ], [8], the functional to minimize taking the form:”
Semi-Supervised Learning with Spectral Graph Wavelets (2011, unknown): “For example, [3, 5] consider: S(f) := ∑ (u,v)∈E wu,v [f(v)− f(u)]2 = fTLf. (1)”
Exclusive Lasso for Multi-task Feature Selection (2010, background): “In (Kowalski et al. 2009, Kowalski 2009), the authors generalized the group lasso by exploring the mixed norm for combining groups of variables.”
Similarity Learning with Spatial Constraints for Person Re-identification (2016, background): “The optimal solution is given by a prox-operator[16]:”
Exact Recovery of Chaotic Systems from Highly Corrupted Data (2016, methodology): “To enforce the group-sparsity with respect to the rows of E [FR08, Kow09, DYZ13], we consider the following optimization model (2.10) For more details, see Section 3.”