Results 1 to 10 of about 6,305 (217)
Annals of Statistics, 2010 This paper develops a theory for group Lasso using a concept called strong group sparsity. Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals. This provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is consistent with the data. Moreover,
Huang, Junzhou, Zhang, T.
exaly +5 more sources
Constructing Measures of Sparsity [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsity that is as broad as possible, so that it generates all the various measures that are useful in practice, but narrow enough that the fundamental properties of generalized sparsity still hold.
Mora-Jiménez, Inmaculada +4 more
openaire +3 more sources
Learning on Hypergraphs with Sparsity [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed.
Nguyen, Canh Hao, Mamitsuka, Hiroshi
openaire +4 more sources
Combinatorica, 2015 We prove that posets of bounded height whose cover graphs belong to a fixed class with bounded expansion have bounded dimension. Bounded expansion, introduced by Nešetřil and Ossona de Mendez as a model for sparsity in graphs, is a property that is naturally satisfied by a wide range of graph classes, from graph structure theory (graphs excluding a ...
Joret, Gwenaël +2 more
openaire +6 more sources
Journal of Spectral Imaging, 2022 Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance.
K. Priya, K. K. Rajkumar
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang +3 more
doaj +1 more source
Dynamic Sparsity Is Channel-Level Sparsity Learner
Advances in Neural Information Processing Systems 36, 2023 Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Lu Yin 0006 +9 more
openaire +4 more sources
Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Mathematics, 2022 As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health.
Yunquan Song, Yaqi Liu, Hang Su
doaj +1 more source
Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
Applied Sciences, 2023 Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features ...
Qinglei Jiang +5 more
doaj +1 more source
When It Counts—Econometric Identification of the Basic Factor Model Based on GLT Structures
Econometrics, 2023 Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT ...
Sylvia Frühwirth-Schnatter +2 more
doaj +1 more source