14.1.4.6 Group LASSO, Trace LASSO

Chapter Contents (Back)
Group Lasso. LASSO Regression. Group lasso allows groups of related covariates to be selected as a single unit. LASSO: Least Absolute Shrinkage and Selection Operator. And a number of application areas.

Panahi, A., Viberg, M.,
Fast Candidate Points Selection in the LASSO Path,
SPLetters(19), No. 2, February 2012, pp. 79-82.
IEEE DOI 1201
BibRef

Xiang, Z.J.[Zhen James], Ramadge, P.J.[Peter J.],
Edge-Preserving Image Regularization Based on Morphological Wavelets and Dyadic Trees,
IP(21), No. 4, April 2012, pp. 1548-1560.
IEEE DOI 1204
BibRef
Earlier:
Morphological wavelet transform with adaptive dyadic structures,
ICIP10(1677-1680).
IEEE DOI 1009
BibRef

Duan, J.[Junbo], Soussen, C., Brie, D., Idier, J., Wang, Y.P.[Yu-Ping],
On LARS/Homotopy Equivalence Conditions for Over-Determined LASSO,
SPLetters(19), No. 12, December 2012, pp. 894-897.
IEEE DOI 1212
LASSO: Least absolute shrinkage and selection operator. BibRef

Jung, A., Hannak, G., Goertz, N.,
Graphical LASSO based Model Selection for Time Series,
SPLetters(22), No. 10, October 2015, pp. 1781-1785.
IEEE DOI 1506
Algorithm design and analysis BibRef

Zhou, Y.[Yun], Han, J.H.[Jiang-Hong], Yuan, X.H.[Xiao-Hui], Wei, Z.C.[Zhen-Chun], Hong, R.C.[Ri-Chang],
Inverse Sparse Group Lasso Model for Robust Object Tracking,
MultMed(19), No. 8, August 2017, pp. 1798-1810.
IEEE DOI 1708
Computational modeling, Dictionaries, Image reconstruction, Object tracking, Robustness, Sparse matrices, hash distance, sparse coding, sparse group lasso, visual, tracking BibRef

Liu, Q.[Qiao], Ma, X.[Xiao], Ou, W.H.[Wei-Hua], Zhou, Q.[Quan],
Visual Object Tracking with Online Sample Selection Via LASSO Regularization,
SIViP(11), No. 5, July 2017, pp. 881-888.
WWW Link. 1706
BibRef

Xiang, Z.J.[Zhen James], Wang, Y.[Yun], Ramadge, P.J.[Peter J.],
Screening Tests for LASSO Problems,
PAMI(39), No. 5, May 2017, pp. 1008-1027.
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
BibRef

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:
WWW Link. BibRef

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.
DOI Link 1103
BibRef

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

Ma, X.[Xiao], Liu, Q.[Qiao], Ou, W.H.[Wei-Hua], Zhou, Q.[Quan],
Visual object tracking via coefficients constrained exclusive group LASSO,
MVA(29), No. 5, July 2018, pp. 749-763.
WWW Link. 1808
BibRef

Xiong, H.[He], Kong, D.[Deguang],
Elastic nonnegative matrix factorization,
PR(90), 2019, pp. 464-475.
Elsevier DOI 1903
NMF, Elastic, Robust, Manifold, Clustering, Exclusive LASSO BibRef

Guo, C.G.[Cheng-Gang], Chen, D.Y.[Dong-Yi], Huang, Z.Q.[Zhi-Qi],
Real-Time Sparse Visual Tracking Using Circulant Reverse LASSO Model,
IEICE(E102-D), No. 1, January 2019, pp. 175-184.
WWW Link. 1901
BibRef

Duan, J.B.[Jun-Bo], Idier, J.[Jérôme], Wang, Y.P.[Yu-Ping], Wan, M.X.[Ming-Xi],
A Joint Least Squares and Least Absolute Deviation Model,
SPLetters(26), No. 4, April 2019, pp. 543-547.
IEEE DOI 1903
LASSO: Least absolute shrinkage and selection operator. least squares approximations, signal restoration, JOLESALAD, generalized LASSO, constrained LASSO, LASSO models, ramp signal restoration BibRef

Dong, Y., Yang, X., Zhao, X., Li, J.,
Bidirectional Convolutional Recurrent Sparse Network (BCRSN): An Efficient Model for Music Emotion Recognition,
MultMed(21), No. 12, December 2019, pp. 3150-3163.
IEEE DOI 1912
Feature extraction, Music, Emotion recognition, Speech recognition, Convolution, Recurrent neural networks, Databases, Lasso regression BibRef

Moghimi, B., Safikhani, A., Kamga, C., Hao, W., Ma, J.,
Short-Term Prediction of Signal Cycle on an Arterial With Actuated-Uncoordinated Control Using Sparse Time Series Models,
ITS(20), No. 8, August 2019, pp. 2976-2985.
IEEE DOI 1908
Time series analysis, Data models, Predictive models, Detectors, Delays, Reactive power, Automobiles, Fully actuated signal, HGLASSO 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], Görnitz, 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.[José], 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

Wang, X., Benesty, J., Chen, J., Cohen, I.,
Beamforming With Small-Spacing Microphone Arrays Using Constrained/Generalized LASSO,
SPLetters(27), 2020, pp. 356-360.
IEEE DOI 2004
Beamforming, white noise gain, directivity factor, signal-to-scattered-interference-ratio gain, LASSO BibRef

Li, D., Cui, F., Wang, A., Li, Y., Wu, J., Qiao, Y.,
Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method,
GeoRS(58), No. 12, December 2020, pp. 8649-8664.
IEEE DOI 2012
Atmospheric measurements, Clouds, Atmospheric modeling, Brightness temperature, Feature extraction, Remote sensing, remote sensing BibRef

Mao, R.Y.[Ru-Yong], Chen, Z.Y.[Zheng-Yu], Hu, G.B.[Guo-Bing],
Robust temporal low-rank representation for traffic data recovery via fused LASSO,
IET-ITS(15), No. 2, 2021, pp. 175-186.
DOI Link 2106
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

Jarret, A.[Adrian], Fageot, J.[Julien], Simeoni, M.[Matthieu],
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO,
SPLetters(29), 2022, pp. 637-641.
IEEE DOI 2203
Signal processing algorithms, Approximation algorithms, Matching pursuit algorithms, Convergence, Optimization, convex optimisation BibRef

Masuda, R.[Ryo], Inoue, R.[Ryo],
Point Event Cluster Detection via the Bayesian Generalized Fused Lasso,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Shang, P.[Pan], Kong, L.C.[Ling-Chen], Liu, D.[Dashuai],
A Safe Feature Screening Rule for Rank Lasso,
SPLetters(29), 2022, pp. 1062-1066.
IEEE DOI 2205
Data models, Computational modeling, Tuning, Computational efficiency, Task analysis, Mathematical models, screening rule BibRef

Gao, R.[Rui], Särkkä, S.[Simo], Claveria-Vega, R.[Rubén], Godsill, S.[Simon],
Autonomous Tracking and State Estimation With Generalized Group Lasso,
Cyber(52), No. 11, November 2022, pp. 12056-12070.
IEEE DOI 2211
State estimation, Minimization, Target tracking, Smoothing methods, Bayes methods, Autonomous vehicles, Vehicle dynamics, state estimation BibRef


Tsiligkaridis, T.[Theodoros], Roberts, J.[Jay],
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training,
CVPR22(50-59)
IEEE DOI 2210
Training, Deep learning, Perturbation methods, Neural networks, Distortion, Robustness, Pattern recognition, Machine learning, Adversarial attack and defense BibRef

Wang, J.Y.[Jian-Yu], Zhang, X.L.[Xiao-Lei],
Deep Topic Modeling by Multilayer Bootstrap Network and Lasso,
ICPR21(2470-2475)
IEEE DOI 2105
Dimensionality reduction, Analytical models, Text analysis, Clustering algorithms, Nonhomogeneous media, Data models, Pattern recognition 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

Tan, H.L.[Han-Lin], Xiao, H.X.[Hua-Xin], Liu, Y.[Yu], Zhang, M.J.[Mao-Jun], Wang, B.[Bin],
LASSO approximation and application to image super-resolution with CUDA acceleration,
ICIVC17(483-488)
IEEE DOI 1708
Acceleration, Dictionaries, Graphics processing units, Image resolution, Inverse problems, Learning systems, Signal resolution, CUDA, LASSO, super-resolution BibRef

Bibi, A., Itani, H., Ghanem, B.[Bernard],
FFTLasso: Large-Scale LASSO in the Fourier Domain,
CVPR17(4371-4380)
IEEE DOI 1711
Convolutional codes, Dictionaries, Encoding, Face recognition, Graphics processing units, Linear systems, Sparse, matrices BibRef

Aliquintuy, M.[Marcelo], Frandi, E.[Emanuele], Ńanculef, 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

Xin, B.[Bo], Tian, Y.[Yuan], Wang, Y.Z.[Yi-Zhou], Gao, W.[Wen],
Background Subtraction via generalized fused LASSO foreground modeling,
CVPR15(4676-4684)
IEEE DOI 1510
BibRef

Zhao, K.[Kaili], Zhang, H.G.[Hong-Gang], Guo, J.[Jun],
An adaptive group LASSO based multi-label regression approach for facial expression analysis,
ICIP14(1435-1439)
IEEE DOI 1502
Algorithm design and analysis BibRef

Zhao, K.[Kaili], Zhang, H.G.[Hong-Gang], Dong, M.Z.[Ming-Zhi], Guo, J.[Jun], Qi, Y.G.[Yong-Gang], Song, Y.Z.[Yi-Zhe],
A multi-label classification approach for Facial Expression Recognition,
VCIP13(1-6)
IEEE DOI 1402
convex programming 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

Zini, L.[Luca], Odone, F.[Francesca],
Efficient pedestrian detection with group lasso,
VS11(1777-1784).
IEEE DOI 1201
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
Locally Linear Embedding, Nonlinear Embedding .


Last update:Jan 30, 2024 at 20:33:16