14.1.3.6.4 Group Lasso, Trace Lasso

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Group Lasso. Group lasso allows groups of related covariates to be selected as a single unit.

Panahi, A., Viberg, M.,
Fast Candidate Points Selection in the LASSO Path,
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Morphological wavelet transform with adaptive dyadic structures,
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Xiang, Z.J.[Zhen James], Wang, Y.[Yun], Ramadge, P.J.[Peter J.],
Screening Tests for Lasso Problems,
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IEEE DOI 1704
Lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. BibRef

Yuan, L.[Lei], Liu, J.[Jun], Ye, J.P.[Jie-Ping],
Efficient Methods for Overlapping Group Lasso,
PAMI(35), No. 9, 2013, pp. 2104-2116.
IEEE DOI 1307
Acceleration. Lasso for feature selection on nonoverlapping features. BibRef

Wang, J., Fan, W., Ye, J.,
Fused Lasso Screening Rules via the Monotonicity of Subdifferentials,
PAMI(37), No. 9, September 2015, pp. 1806-1820.
IEEE DOI 1508
Computational efficiency. Lasso: Least absolute shrinkage and selection operator. BibRef

Zhao, L., Hu, Q., Wang, W.,
Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO,
MultMed(17), No. 11, November 2015, pp. 1936-1948.
IEEE DOI 1511
Data mining BibRef

Souly, N.[Nasim], Shah, M.A.[Mubarak A.],
Visual Saliency Detection Using Group Lasso Regularization in Videos of Natural Scenes,
IJCV(117), No. 1, March 2016, pp. 93-110.
Springer DOI 1604
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Shen, X.Y.[Xin-Yue], Chen, L.[Laming], Gu, Y.T.[Yuan-Tao], So, H.C.,
Square-Root Lasso With Nonconvex Regularization: An ADMM Approach,
SPLetters(23), No. 7, July 2016, pp. 934-938.
IEEE DOI 1608
LASSO: least absolute shrinkage and selection operator. concave programming BibRef

Painsky, A., Rosset, S.,
Isotonic Modeling with Non-Differentiable Loss Functions with Application to Lasso Regularization,
PAMI(38), No. 2, February 2016, pp. 308-321.
IEEE DOI 1601
Algorithm design and analysis Code, Regularization. Implementation:
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Lu, G.F.[Gui-Fu], Lin, Z.[Zhong], Jin, Z.[Zhong],
Face recognition using discriminant locality preserving projections based on maximum margin criterion,
PR(43), No. 10, October 2010, pp. 3572-3579.
Elsevier DOI 1007
MMC; Locality preserving; Small sample size problem; Feature extraction; Face recognition BibRef

Lu, G.F.[Gui-Fu], Lin, Z.[Zhong], Jin, Z.[Zhong],
Face recognition using regularised generalised discriminant locality preserving projections,
IET-CV(5), No. 2, 2011, pp. 107-116.
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Lu, G.F.[Gui-Fu], Tang, G.[Ganyi], Zou, J.[Jian],
Spare L1-norm-based maximum margin criterion,
JVCIR(38), No. 1, 2016, pp. 11-17.
Elsevier DOI 1605
Feature extraction BibRef

Lu, G.F.[Gui-Fu], Zou, J.[Jian], Wang, Y.[Yong],
L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso,
PR(55), No. 1, 2016, pp. 207-214.
Elsevier DOI 1604
Discriminant locality preserving projections BibRef

Zhang, Z.H.[Zhi-Hong], Tian, Y.Y.[Yi-Yang], Bai, L.[Lu], Xiahou, J.B.[Jian-Bing], Hancock, E.R.[Edwin R.],
High-order covariate interacted Lasso for feature selection,
PRL(87), No. 1, 2017, pp. 139-146.
Elsevier DOI 1703
Lasso BibRef

Liu, C.[Cheng], Zheng, C.T.[Chu-Tao], Qian, S.[Sheng], Wu, S.[Si], Wong, H.S.[Hau-San],
Encoding sparse and competitive structures among tasks in multi-task learning,
PR(88), 2019, pp. 689-701.
Elsevier DOI 1901
Multi-task learning, Sparse exclusive lasso, Task-competitive BibRef

Seghouane, A.K.[Abd-Krim], Shokouhi, N.[Navid], Koch, I.[Inge],
Sparse Principal Component Analysis With Preserved Sparsity Pattern,
IP(28), No. 7, July 2019, pp. 3274-3285.
IEEE DOI 1906
biomedical MRI, blind source separation, data analysis, pattern recognition, principal component analysis, group lasso BibRef

Abdolali, M.[Maryam], Rahmati, M.[Mohammad],
Robust subspace clustering for image data using clean dictionary estimation and group lasso based matrix completion,
JVCIR(61), 2019, pp. 303-314.
Elsevier DOI 1906
Subspace estimation, Sparse representation, Sparse subspace clustering, Group lasso, Matrix completion BibRef

Zhang, M.[Mimi],
Forward-stagewise clustering: An algorithm for convex clustering,
PRL(128), 2019, pp. 283-289.
Elsevier DOI 1912
Fusion penalty, Generalized lasso, Hierarchical clustering, K-nearest neighbor BibRef

Zheng, S.[Shuai], Ding, C.[Chris],
A group lasso based sparse KNN classifier,
PRL(131), 2020, pp. 227-233.
Elsevier DOI 2004
Sparse learning, Group lasso, Explainable classifier BibRef

Cui, L.X.[Li-Xin], Bai, L.[Lu], Wang, Y.[Yue], Yu, P.S.[Philip S.], Hancock, E.R.[Edwin R.],
Fused lasso for feature selection using structural information,
PR(119), 2021, pp. 108058.
Elsevier DOI 2106
Feature selection, Structural relationship, Fused lasso, Graph-based feature selection, Sparse learning, Correlated feature group BibRef

Lee, S.[Seunghak], GŲrnitz, N.[Nico], Xing, E.P.[Eric P.], Heckerman, D.[David], Lippert, C.[Christoph],
Ensembles of Lasso Screening Rules,
PAMI(40), No. 12, December 2018, pp. 2841-2852.
IEEE DOI 1811
Closed-form solutions, Heuristic algorithms, Algorithm design and analysis, Feature extraction, ensemble BibRef

Bento, J.[Josť], Furmaniak, R.[Ralph], Ray, S.[Surjyendu],
On the Complexity of the Weighted Fused Lasso,
SPLetters(25), No. 10, October 2018, pp. 1595-1599.
IEEE DOI 1810
dynamic programming, least squares approximations, piecewise linear techniques, string matching, weights BibRef

Fosson, S.M.,
A Biconvex Analysis for Lasso L_1 Reweighting,
SPLetters(25), No. 12, December 2018, pp. 1795-1799.
IEEE DOI 1812
compressed sensing, convergence of numerical methods, convex programming, iterative methods, regression analysis, reweighting algorithms BibRef

Ren, S.G.[Shao-Gang], Huang, S.A.[Shu-Ai], Ye, J.P.[Jie-Ping], Qian, X.N.[Xiao-Ning],
Safe Feature Screening for Generalized LASSO,
PAMI(40), No. 12, December 2018, pp. 2992-3006.
IEEE DOI 1811
Optimization, Estimation, Sparse matrices, Linear regression, Logistics, Heuristic algorithms, Feature detection, feature screening BibRef

Jung, A.[Alexander],
On the Duality Between Network Flows and Network Lasso,
SPLetters(27), 2020, pp. 940-944.
IEEE DOI 2007
TV, Optimization, Minimization, Data models, Linear programming, Clustering algorithms, Signal processing algorithms, optimization methods BibRef

Jung, A., Sarcheshmeh Pour, Y.,
Local Graph Clustering With Network Lasso,
SPLetters(28), 2021, pp. 106-110.
IEEE DOI 2101
TV, Clustering methods, Optimization, Minimization, Laplace equations, Message passing, Convergence, semisupervised learning BibRef


Seghouane, A.K., Qadar, M.A.,
Sparsity Preserved Canonical Correlation Analysis,
ICIP20(31-35)
IEEE DOI 2011
Loading, Correlation, Matrix decomposition, Functional magnetic resonance imaging, Data analysis, group lasso. BibRef

Oyedotun, O.K., Aouada, D., Ottersten, B.,
Structured Compression of Deep Neural Networks with Debiased Elastic Group LASSO,
WACV20(2266-2275)
IEEE DOI 2006
Computational modeling, Feature extraction, Training, Cost function, Training data, Task analysis, Neural networks BibRef

Alshawaqfeh, M.[Mustafa], Al Kawam, A.[Ahmad], Serpedin, E.[Erchin],
Robust Fussed Lasso Model for Recurrent Copy Number Variation Detection,
ICPR18(3772-3777)
IEEE DOI 1812
Probes, Sparse matrices, Mathematical model, DNA, Matrix decomposition, Diseases, Adaptation models BibRef

Aliquintuy, M.[Marcelo], Frandi, E.[Emanuele], —anculef, R.[Ricardo], Suykens, J.A.K.[Johan A. K.],
Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso,
CIARP16(208-216).
Springer DOI 1703
BibRef

Li, Q.[Qiang], Qiao, M.Y.[Mao-Ying], Bian, W.[Wei], Tao, D.C.[Da-Cheng],
Conditional Graphical Lasso for Multi-label Image Classification,
CVPR16(2977-2986)
IEEE DOI 1612
BibRef

Nafornita, C., Isar, A., Nelson, J.D.B.,
Regularised, semi-local hurst estimation via generalised lasso and dual-tree complex wavelets,
ICIP14(2689-2693)
IEEE DOI 1502
Estimation BibRef

Hung, T.Y.[Tzu-Yi], Lu, J.W.[Ji-Wen], Tan, Y.P.[Yap-Peng], Gao, S.H.[Sheng-Hua],
Efficient Sparsity Estimation via Marginal-Lasso Coding,
ECCV14(IV: 578-592).
Springer DOI 1408
BibRef

Vogt, J.E.[Julia E.], Roth, V.[Volker],
The Group-Lasso: L1,inf Regularization versus L1,2 Regularization,
DAGM10(252-261).
Springer DOI 1009
Award, GCPR, HM. BibRef

Wang, J.[Jing], Su, G.D.[Guang-Da], Chen, J.S.[Jian-Sheng], Moon, Y.S.[Yiu-Sang],
CPGL: A classification method combining PCA and the Group Lasso method,
ICIP10(4529-4532).
IEEE DOI 1009
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Discriminant Analysis .


Last update:Oct 20, 2021 at 09:45:26