13.3.8.5 Regression

Yun, W.H.[Woo-Han], Bang, S.Y.[Sung Yang], Kim, D.J.[Dai-Jin],
Real-time Object Recognition using Relational Dependency based on Graphical Model,
PR(41), No. 2, February 2008, pp. 742-753.
Elsevier DOI 0711
BibRef
Earlier: ICPR06(III: 28-32).
IEEE DOI 0609
Object recognition; Graphical model; Relational dependency; Logistic regression; The cascaded adaboost detector; Transition matrix BibRef

Liu, G.C.[Guang-Can], Lin, Z.C.[Zhou-Chen], Yu, Y.[Yong],
Multi-output regression on the output manifold,
PR(42), No. 11, November 2009, pp. 2737-2743.
Elsevier DOI 0907
Regression analysis; Support vector regression; Continuous structured prediction; Manifold learning BibRef

Simila, T.[Timo], Tikka, J.[Jarkko],
Combined input variable selection and model complexity control for nonlinear regression,
PRL(30), No. 3, 1 February 2009, pp. 231-236.
Elsevier DOI 0804
Regression; Function approximation; MLP; Multilayer perceptron; Input variable selection; Hidden node selection BibRef

Domingues, M.A.O.[Marco A.O.], de Souza, R.M.C.R.[Renata M.C.R.], Cysneiros, F.J.A.[Francisco Jose A.],
A robust method for linear regression of symbolic interval data,
PRL(31), No. 13, 1 October 2010, pp. 1991-1996.
Elsevier DOI 1003
Symbolic interval-valued data; Symbolic data analysis; Symmetrical linear regression; Outliers BibRef

Ranganathan, A.[Ananth], Yang, M.H.[Ming-Hsuan], Ho, J.,
Online Sparse Gaussian Process Regression and Its Applications,
IP(19), No. 2, February 2011, pp. 391-404.
IEEE DOI 1102
BibRef
Earlier: A1, A2, Only:
Online Sparse Matrix Gaussian Process Regression and Vision Applications,
ECCV08(I: 468-482).
Springer DOI 0810
BibRef

Guo, W.W.[Wei-Wei], Kotsia, I.[Irene], Patras, I.[Ioannis],
Tensor Learning for Regression,
IP(21), No. 2, February 2012, pp. 816-827.
IEEE DOI 1201
BibRef

Kotsia, I.[Irene], Guo, W.W.[Wei-Wei], Patras, I.[Ioannis],
Higher rank Support Tensor Machines for visual recognition,
PR(45), No. 12, December 2012, pp. 4192-4203.
Elsevier DOI 1208
BibRef
And:
Higher Rank Support Tensor Machines,
ISVC12(II: 31-40).
Springer DOI 1209
Support Tensor Machines; CANDECOMP/PARAFAC tensor decomposition; Relative Margin Support Vector Machines; Action recognition; Gait recognition BibRef

Garcia, E., Arora, R., Gupta, M.R.,
Optimized Regression for Efficient Function Evaluation,
IP(21), No. 9, September 2012, pp. 4128-4140.
IEEE DOI 1208
BibRef

Liang, Z.Z.[Zhi-Zheng], Li, Y.F.[You-Fu], Xia, S.X.[Shi-Xiong],
Adaptive weighted learning for linear regression problems via Kullback-Leibler divergence,
PR(46), No. 4, April 2013, pp. 1209-1219.
Elsevier DOI 1301
Linear regression; KL divergence; Weighted learning; Alternative optimization; Image classification BibRef

Shen, F., Shen, C.H., van den Hengel, A.J., Tang, Z.,
Approximate Least Trimmed Sum of Squares Fitting and Applications in Image Analysis,
IP(22), No. 5, May 2013, pp. 1836-1847.
IEEE DOI 1303
regression estimation criterion. BibRef

Rodrigues, F.[Filipe], Pereira, F.C.[Francisco C.], Ribeiro, B.[Bernardete],
Learning from multiple annotators: Distinguishing good from random labelers,
PRL(34), No. 12, 1 September 2013, pp. 1428-1436.
Elsevier DOI 1306
Multiple annotators; Crowdsourcing; Latent variable models; Expectation-Maximization; Logistic Regression BibRef

Rodrigues, F.[Filipe], Lourenço, M., Ribeiro, B.[Bernardete], Pereira, F.C.[Francisco C.],
Learning Supervised Topic Models for Classification and Regression from Crowds,
PAMI(39), No. 12, December 2017, pp. 2409-2422.
IEEE DOI 1711
Analytical models, Data models, Inference algorithms, Predictive models, Stochastic processes, Topic models, crowdsoucing. BibRef

Fletcher, P.T.[P. Thomas],
Geodesic Regression and the Theory of Least Squares on Riemannian Manifolds,
IJCV(105), No. 2, November 2013, pp. 171-185.
Springer DOI 1309
BibRef

Hinkle, J.[Jacob], Fletcher, P.T.[P. Thomas], Joshi, S.[Sarang],
Intrinsic Polynomials for Regression on Riemannian Manifolds,
JMIV(50), No. 1-2, September 2014, pp. 32-52.
Springer DOI 1408
BibRef

Hinkle, J.[Jacob], Muralidharan, P.[Prasanna], Fletcher, P.T.[P. Thomas], Joshi, S.[Sarang],
Polynomial Regression on Riemannian Manifolds,
ECCV12(III: 1-14).
Springer DOI 1210
BibRef

Huber, M.F.[Marco F.],
Recursive Gaussian process: On-line regression and learning,
PRL(45), No. 1, 2014, pp. 85-91.
Elsevier DOI 1407
Gaussian processes BibRef

Zhu, W.[Wentao], Miao, J.[Jun], Qing, L.Y.[Lai-Yun],
Robust regression with extreme support vectors,
PRL(45), No. 1, 2014, pp. 205-210.
Elsevier DOI 1407
Extreme Support Vector Regression BibRef

Schaeben, H.[Helmut],
Targeting: Logistic Regression, Special Cases and Extensions,
IJGI(3), No. 4, 2014, pp. 1387-1411.
DOI Link 1412
Recover the true conditional probabilities if the joint distribution. BibRef

Zou, B.[Bin], Tang, Y.Y.[Yuan Yan], Xu, Z.B.[Zong-Ben], Li, L.Q.[Luo-Qing], Xu, J.[Jie], Lu, Y.[Yang],
The Generalization Performance of Regularized Regression Algorithms Based on Markov Sampling,
Cyber(44), No. 9, September 2014, pp. 1497-1507.
IEEE DOI 1410
Markov processes BibRef

Bootkrajang, J.[Jakramate], Kaban, A.[Ata],
Learning kernel logistic regression in the presence of class label noise,
PR(47), No. 11, 2014, pp. 3641-3655.
Elsevier DOI 1407
Classification BibRef

Yang, H., Jia, X., Patras, I., Chan, K.P.,
Random Subspace Supervised Descent Method for Regression Problems in Computer Vision,
SPLetters(22), No. 10, October 2015, pp. 1816-1820.
IEEE DOI 1506
Computer vision BibRef

Pan, Z.[Zheng], Zhang, C.S.[Chang-Shui],
Relaxed sparse eigenvalue conditions for sparse estimation via non-convex regularized regression,
PR(48), No. 1, 2015, pp. 231-243.
Elsevier DOI 1410
Sparse estimation BibRef

Munoz-Gonzalez, L., Lazaro-Gredilla, M., Figueiras-Vidal, A.R.,
Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression,
PAMI(38), No. 3, March 2016, pp. 618-624.
IEEE DOI 1602
Approximation algorithms BibRef

Pan, L.[Lili], Saragih, J.M.[Jason M.], Chu, W.S.[Wen-Sheng],
Mixture of grouped regressors and its application to visual mapping,
PR(53), No. 1, 2016, pp. 184-194.
Elsevier DOI 1602
Mixture of regressors BibRef

Nguyen, H.D., Lloyd-Jones, L.R., McLachlan, G.J.,
A Block Minorization-Maximization Algorithm for Heteroscedastic Regression,
SPLetters(23), No. 8, August 2016, pp. 1131-1135.
IEEE DOI 1608
Big Data BibRef

Li, X., Zheng, J.,
Active Learning for Regression With Correlation Matching and Labeling Error Suppression,
SPLetters(23), No. 8, August 2016, pp. 1081-1085.
IEEE DOI 1608
compressed sensing BibRef

Hong, Y.[Yi], Kwitt, R.[Roland], Singh, N.[Nikhil], Vasconcelos, N.[Nuno], Niethammer, M.[Marc],
Parametric Regression on the Grassmannian,
PAMI(38), No. 11, November 2016, pp. 2284-2297.
IEEE DOI 1610
Jacobian matrices BibRef

Hong, Y.[Yi], Kwitt, R.[Roland], Singh, N.[Nikhil], Davis, B.[Brad], Vasconcelos, N.[Nuno], Niethammer, M.[Marc],
Geodesic Regression on the Grassmannian,
ECCV14(II: 632-646).
Springer DOI 1408
BibRef

Gu, Y.X.[Ying-Xin], Wylie, B.K.[Bruce K.], Boyte, S.P.[Stephen P.], Picotte, J.[Joshua], Howard, D.M.[Daniel M.], Smith, K.[Kelcy], Nelson, K.J.[Kurtis J.],
An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data,
RS(8), No. 11, 2016, pp. 943.
DOI Link 1612
BibRef

Neyshabouri, M.M.[Mohammadreza Mohaghegh], Demir, O.[Oguzhan], Delibalta, I.[Ibrahim], Kozat, S.S.[Suleyman Serdar],
Highly efficient nonlinear regression for big data with lexicographical splitting,
SIViP(11), No. 3, March 2017, pp. 391-398.
Springer DOI 1702
BibRef

Zhang, Z., Lai, Z., Xu, Y., Shao, L., Wu, J., Xie, G.S.,
Discriminative Elastic-Net Regularized Linear Regression,
IP(26), No. 3, March 2017, pp. 1466-1481.
IEEE DOI 1703
image classification BibRef

Tehrani, A.F.[Ali Fallah], Ahrens, D.[Diane],
Modeling label dependence for multi-label classification using the Choquistic regression,
PRL(92), No. 1, 2017, pp. 75-80.
Elsevier DOI 1705
Choquet integral BibRef

Golay, J.[Jean], Leuenberger, M.[Michael], Kanevski, M.[Mikhail],
Feature selection for regression problems based on the Morisita estimator of intrinsic dimension,
PR(70), No. 1, 2017, pp. 126-138.
Elsevier DOI 1706
Feature, selection BibRef

Kim, K.[Kyoungok], Hong, J.S.[Jung-Sik],
A hybrid decision tree algorithm for mixed numeric and categorical data in regression analysis,
PRL(98), No. 1, 2017, pp. 39-45.
Elsevier DOI 1710
Mixed data BibRef

Gillis, N., Luce, R.,
A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary,
IP(27), No. 1, January 2018, pp. 24-37.
IEEE DOI 1712
convex programming, gradient methods, matrix decomposition, regression analysis, NMF, conic basis columns, data matrix, sparse regression BibRef

Zhen, X.T.[Xian-Tong], Yu, M.Y.[Meng-Yang], He, X.F.[Xiao-Fei], Li, S.[Shuo],
Multi-Target Regression via Robust Low-Rank Learning,
PAMI(40), No. 2, February 2018, pp. 497-504.
IEEE DOI 1801
Biomedical imaging, Computational modeling, Correlation, Data models, Kernel, Optimization, Robustness, multi-target regression BibRef

Martins, P., Batista, I.J.,
Simultaneous Cascaded Regression,
ICIP18(181-185)
IEEE DOI 1809
Shape, Jacobian matrices, Optimization, Face, Computational modeling, Integrated circuits, Deformable models, cascaded regression BibRef

Zhang, Z., Zhong, Z., Cui, J., Fei, L.,
Learning robust latent subspace for discriminative regression,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, learning (artificial intelligence), mathematics computing, optimisation, pattern classification, sparse BibRef

Wu, H., Spurlock, S., Souvenir, R.,
Semi-supervised multi-output image manifold regression,
ICIP17(2413-2417)
IEEE DOI 1803
Kernel, Manifolds, Mathematical model, Prediction algorithms, Principal component analysis, Task analysis, semi-supervised BibRef

Berthoumieu, Y., Bombrun, L., Germain, C., Said, S.,
Classification approach based on the product of riemannian manifolds from Gaussian parametrization space,
ICIP17(206-210)
IEEE DOI 1803
Covariance matrices, Databases, Gaussian distribution, Manifolds, Measurement, Task analysis, Visualization, Classification, image local descriptors BibRef

Muralidharan, P., Hinkle, J., Fletcher, P.T.,
A map estimation algorithm for Bayesian polynomial regression on riemannian manifolds,
ICIP17(215-219)
IEEE DOI 1803
Bayes methods, Data models, Manifolds, Maximum likelihood estimation, Rats, Shape BibRef

Gorbach, N.S.[Nico S.], Bian, A.A.[Andrew An], Fischer, B.[Benjamin], Bauer, S.[Stefan], Buhmann, J.M.[Joachim M.],
Model Selection for Gaussian Process Regression,
GCPR17(306-318).
Springer DOI 1711
BibRef

Yoo, Y., Yun, S., Chang, H.J., Demiris, Y., Choi, J.Y.,
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold,
CVPR17(2943-2952)
IEEE DOI 1711
Data models, Decoding, Gaussian processes, Image reconstruction, Image sequences, Kernel, Visualization BibRef

Huang, D., Han, L., de la Torre, F.[Fernando],
Soft-Margin Mixture of Regressions,
CVPR17(4058-4066)
IEEE DOI 1711
Estimation, Gaussian processes, Ground penetrating radar, Kernel, Magnetic resonance, Training BibRef

Maier, A., Rodríguez-Salas, D.,
Fast and robust selection of highly-correlated features in regression problems,
MVA17(482-485)
DOI Link 1708
Algorithm design and analysis, Correlation, Linear regression, Prediction algorithms, Robustness, Search problems, Weight, measurement BibRef

Luo, L.[Lei], Tu, Q.H.[Qing-Hua], Yang, J.[Jian], Zhang, Y.G.[Yi-Gong],
Dual approximated nuclear norm based matrix regression via adaptive line search scheme,
ICPR16(2538-2543)
IEEE DOI 1705
Acceleration, Convergence, Face recognition, Matrix converters, Nuclear magnetic resonance, Robustness, adaptive line search scheme, continuously differentiable, face recognition, gradient method, nuclear, norm, based, matrix, regression BibRef

Fagundes, R.A.A., de Souza, R.M.C.R.[Renata M.C.R.], Soares, Y.M.G.,
Quantile regression of interval-valued data,
ICPR16(2586-2591)
IEEE DOI 1705
Analytical models, Computational modeling, Data models, Linear regression, Mathematical model, Predictive models, Production BibRef

Subramanian, S., Rana, S., Gupta, S., Sivakumar, P.B., Velayutham, C.S., Venkateshc, S.,
Bayesian nonparametric Multiple Instance Regression,
ICPR16(3661-3666)
IEEE DOI 1705
Agriculture, Bayes methods, Clustering algorithms, Computational modeling, Data models, Prediction algorithms, Predictive, models BibRef

Xing, C., Geng, X., Xue, H.,
Logistic Boosting Regression for Label Distribution Learning,
CVPR16(4489-4497)
IEEE DOI 1612
BibRef

Feng, Q., Zhou, Y., Lan, R.,
Pairwise Linear Regression Classification for Image Set Retrieval,
CVPR16(4865-4872)
IEEE DOI 1612
BibRef

Banerjee, M.[Monami], Chakraborty, R.[Rudrasis], Ofori, E.[Edward], Okun, M.S.[Michael S.], Vaillancourt, D.E.[David E.], Vemuri, B.C.[Baba C.],
A Nonlinear Regression Technique for Manifold Valued Data with Applications to Medical Image Analysis,
CVPR16(4424-4432)
IEEE DOI 1612
BibRef

Wang, Y.X.[Yu-Xiong], Hebert, M.[Martial],
Learning to Learn: Model Regression Networks for Easy Small Sample Learning,
ECCV16(VI: 616-634).
Springer DOI 1611
BibRef

Robles-Kelly, A.[Antonio],
Least-Squares Regression with Unitary Constraints for Network Behaviour Classification,
SSSPR16(26-36).
Springer DOI 1611
BibRef

Koda, S.[Satoru],
Adaptive Sparse Bayesian Regression with Variational Inference for Parameter Estimation,
SSSPR16(263-273).
Springer DOI 1611
BibRef

Antonakos, E., Snape, P., Trigeorgis, G., Zafeiriou, S.P.[Stefanos P.],
Adaptive cascaded regression,
ICIP16(1649-1653)
IEEE DOI 1610
Active appearance model BibRef

Serra, J.G., Ruiz, P., Molina, R., Katsaggelos, A.K.,
Bayesian logistic regression with sparse general representation prior for multispectral image classification,
ICIP16(1893-1897)
IEEE DOI 1610
Adaptation models BibRef

Gurovich, Y., Kissos, I., Hanani, Y.,
Quality scores for deep regression systems,
ICIP16(3758-3762)
IEEE DOI 1610
Face BibRef

Perez-Pellitero, E., Salvador, J., Ruiz-Hidalgo, J., Rosenhahn, B.,
Half hypersphere confinement for piecewise linear regression,
WACV16(1-9)
IEEE DOI 1606
Euclidean distance BibRef

Belagiannis, V., Rupprecht, C., Carneiro, G., Navab, N.,
Robust Optimization for Deep Regression,
ICCV15(2830-2838)
IEEE DOI 1602
Convergence; Machine learning; Minimization; Robustness; Training BibRef

Pandremmenou, K.[Katerina], Shahid, M.[Muhammad], Kondi, L.P.[Lisimachos P.], Lovstrom, B.[Benny],
On the improvement of no-reference mean opinion score estimation accuracy by following a frame-level regression approach,
ICIP15(1850-1854)
IEEE DOI 1512
Estimation accuracy BibRef

Wang, Y.[Yin], Dicle, C.[Caglayan], Sznaier, M.[Mario], Camps, O.I.[Octavia I.],
Self Scaled Regularized Robust Regression,
CVPR15(3261-3269)
IEEE DOI 1510
BibRef

Nilsson, M.[Mikael],
Elastic Net Regularized Logistic Regression Using Cubic Majorization,
ICPR14(3446-3451)
IEEE DOI 1412
Convergence BibRef

Saragih, J.M.[Jason M.],
Principal regression analysis,
CVPR11(2881-2888).
IEEE DOI 1106
Multivariate regression. For non-rigid face and car alignment. BibRef

Raman, S.[Sudhir], Roth, V.[Volker],
Sparse Point Estimation for Bayesian Regression via Simulated Annealing,
DAGM12(317-326).
Springer DOI 1209
BibRef
Earlier:
Sparse Bayesian Regression for Grouped Variables in Generalized Linear Models,
DAGM09(242-251).
Springer DOI 0909
BibRef

Hong, Y.[Yi], Shi, Y.[Yundi], Styner, M.[Martin], Sanchez, M.[Mar], Niethammer, M.[Marc],
Simple Geodesic Regression for Image Time-Series,
WBIR12(11-20).
Springer DOI 1208
BibRef

Kramer, O.[Oliver],
On Evolutionary Approaches to Unsupervised Nearest Neighbor Regression,
EvoIASP(346-355).
Springer DOI 1204
BibRef

Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok], Chellappa, R.[Rama],
Robust RVM regression using sparse outlier model,
CVPR10(1887-1894).
IEEE DOI 1006
Relevance Vector Machine. BibRef

Yang, G.[Gelan], Xu, X.[Xue], Jin, H.X.[Hui-Xia],
Semi-Supervised Regression via Local Block Coordinate,
CISP09(1-4).
IEEE DOI 0910
BibRef

Ding, L.[Lei], Bai, X.L.[Xiao-Le],
Adaptive Laplacian eigenfunctions as bases for regression analysis,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
MRF Optimization, Energy Minimization .