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Discriminative Elastic-Net Regularized Linear Regression
2017
IEEE Transactions on Image Processing
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To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. ...
Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. ...
Based on the elastic-net regularized linear regression framework in Eq. (5), two robust elastic-net regularized linear regression methods are proposed, i.e. discriminative elasticnet regularized linear ...
doi:10.1109/tip.2017.2651396
pmid:28092552
fatcat:lb3ljuqwwrfl3o3c27pkii6yvi
Fusion of Logically Concatenated Cross Binary Pattern and ε-Dragging Linear Regression for Face Classification across Poses
2020
International Journal of Machine Learning and Computing
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Index Terms-Discriminative elastic-net regularization, logically concatenated cross binary pattern, marginalized elastic-net regularized linear regression, negative dragging linear regression. ...
In order to retain only discriminant features, a balanced approach, like Marginalized Elastic-Net Regularization with ε-dragging, is fixed into the regularization term of the linear regression model. ...
In Section II, work related to popular face descriptors and the elastic-net regularized linear regression framework is discussed. ...
doi:10.18178/ijmlc.2020.10.1.906
fatcat:fvebswlg4ndhtmu6q7bw7dgena
Cox Regression with Correlation Based Regularization for Electronic Health Records
2013
2013 IEEE 13th International Conference on Data Mining
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In addition, we extensively compare our results with other regularized linear and logistic regression algorithms. ...
We demonstrate through our experiments that these regularizers effectively enhance the ability of cox regression to handle correlated features. ...
SLEP [16] package is used to implement the linear regression regularized lasso and elastic net algorithms along with the FLA algorithm. ...
doi:10.1109/icdm.2013.89
dblp:conf/icdm/VinzamuriR13
fatcat:tyt33zdkvvdhvlhowij6raoxve
Dimension reduction of hyperspectral images with sparse linear discriminant analysis
2011
2011 IEEE International Geoscience and Remote Sensing Symposium
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In this paper, a dimension reduction method based on sparse penalty regularized linear discriminant analysis was experimented on hyperspectral data. ...
Through imposing sparsity regularization penalty on the Fisher's discriminant analysis projection matrix via the optimal scoring technique, sparse linear discriminant vectors can be achieved. ...
The subsequent experimental analysis aimed at analyzing the effectiveness of elastic-net regularized linear discriminant analysis in classification the hyperspectral data with projected data, selected ...
doi:10.1109/igarss.2011.6049828
dblp:conf/igarss/LiQ11
fatcat:rzstq22u35h2ngt2pqzurdo5ju
Generalized Sparse Classifiers for Decoding Cognitive States in fMRI
[chapter]
2010
Lecture Notes in Computer Science
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Building on this regularized regression framework, we exploit an extension of elastic net that permits general properties, such as spatial smoothness, to be integrated. ...
GSC draws upon the recognition that numerous standard classifiers can be reformulated under a regression framework, which enables state-of-theart regularization techniques, e.g. elastic net, to be directly ...
For comparison, we also applied LDA [7] , linear SVM [5, 8, 9] , sparse LDA (SLDA), and LDA with elastic net regularization (EN-LDA) to the StarPlus data. ...
doi:10.1007/978-3-642-15948-0_14
fatcat:2xgs6wxqorblvggqztehtwhvnm
Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression
2017
Frontiers in Aging Neuroscience
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Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the " ...
Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain ...
using regularized logistic regression with an elastic net penalty compared with a classical stepwise logistic regression. ...
doi:10.3389/fnagi.2016.00318
pmid:28101051
pmcid:PMC5209379
fatcat:c5n77bhfnjb5vnofjuxgxwv3vy
Active Learning based Survival Regression for Censored Data
2014
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
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Experimental evaluation against state of the art survival regression methods indicates the higher discriminative ability of the proposed approach. ...
With this motivation, we address this problem by providing an active learning based survival model which uses a novel model discriminative gradient based sampling scheme. ...
Cox regression framework which can use any standard regularizer such as the LASSO, elastic net and kernel elastic net. ...
doi:10.1145/2661829.2662065
dblp:conf/cikm/VinzamuriLR14
fatcat:rhm6efl6wnfzfl5trpcsb4i2pu
Biologically-inspired object recognition system for recognizing natural scene categories
2016
2016 International Conference for Students on Applied Engineering (ISCAE)
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An elastic-net regularizer for dictionary learning was presented in [19] . ...
Furthermore, using the elastic net in different layers of En-HMAX enhanced the discriminative power toward highly correlated. ...
doi:10.1109/icsae.2016.7810174
fatcat:c3ltetqgiffm3egroirwnf44n4
Elastic Net based Feature Ranking and Selection
[article]
2020
arXiv
pre-print
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Elastic net is one of the most widely used feature selectors. ...
However, the features selected are dependant on the training data, and their weights dedicated for regularized regression are irrelevant to their importance if used for feature ranking, that degrades the ...
Elastic net is a regularized regression method [8] which adds a L 2 penalty linearly to overcome the limitations of least absolute shrinkage and selection operator (LASSO) [9] . ...
arXiv:2012.14982v1
fatcat:ra4snj3gbnaojexyqlrq5fgb3a
Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints
2018
Iranian Biomedical Journal
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analysis (PLS-DA) [9] , sparse linear discriminant analysis (SLDA) [19] , elastic net-type regularized regression [20] , and smoothly clipped absolute deviation (SCAD) [21] have been established ...
Elastic net regression analysis The elastic net-type regularized regression (e.g., ridge [22] , lasso [17] , elastic net [20] , etc.) is a popular data analysis method for identifying features based ...
pmid:29523019
fatcat:m3vkfzn5bvfkdjbwxuulyqz3zi
fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics
[chapter]
2013
Lecture Notes in Computer Science
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To control the capacity of the resulting prediction function, we utilize the elastic net sparsity regularizer. ...
We validate our method on a cocaine addiction dataset showing a significant improvement over elastic net and kernel ridge regression baselines and a reduction in classification error of over 14%. ...
We control the complexity of our prediction while modeling non-linear interactions between voxels by adding a sparsity regularizer using the elastic net [11] . ...
doi:10.1007/978-3-319-02267-3_12
fatcat:gevdem4labfmvc2htdl4l4edni
Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints
2018
Iranian Biomedical Journal
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Methods: Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net (EN) regression methods. ...
analysis (PLS-DA) [9] , sparse linear discriminant analysis (SLDA) [19] , elastic net-type regularized regression [20] , and smoothly clipped absolute deviation (SCAD) [21] have been established ...
Elastic net regression analysis The elastic net-type regularized regression (e.g., ridge [22] , lasso [17] , elastic net [20] , etc.) is a popular data analysis method for identifying features based ...
doi:10.29252/.22.6.374
fatcat:mxdk5wfzjvbztkua2gm53v6ffm
Total Variation Regularization for fMRI-Based Prediction of Behavior
2011
IEEE Transactions on Medical Imaging
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Moreover, this article presents the first use of TV regularization for classification. ...
To cope with the high dimensionality of the data, the learning method has to be regularized. ...
Among them are linear discriminant analysis [9] , support/relevance vector machines [10] , neural networks [17] , Lasso [18] , elastic net regression [19] , kernel ridge regression [20] , boosting ...
doi:10.1109/tmi.2011.2113378
pmid:21317080
pmcid:PMC3336110
fatcat:cv3g56r3zrf67psjwllh2xbwmm
Total Variation Regularization Enhances Regression-Based Brain Activity Prediction
2010
2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging
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We show on real fMRI data that this method yields more accurate predictions in inter-subject analysis compared to voxel-based reference methods, such as Elastic net or Support Vector Regression. ...
Among other alternatives, 1 regularization achieves simultaneously a selection of the most predictive features. ...
Many methods have been tested for classification or regression of fMRI activation images (Linear Discriminant Analysis, Support/Relevance Vector Machines, Lasso, Elastic net regression and many others) ...
doi:10.1109/wbd.2010.13
fatcat:2svcexs52bf6daxkckxigrukrq
A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data
[article]
2016
arXiv
pre-print
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Therefore we introduced a novel feature selection method which combines a recent implementation of the stability selection approach and the elastic net approach. ...
Elastic net [7] is based on a hybrid of ℓ 1 regularization and ℓ 2 regularization and is applied to linear models here. ...
, ℓ 1 Logistic Regression, randomized ℓ 1 Logistic Regression and the Elastic Net. ...
arXiv:1506.08301v2
fatcat:l5rhio2wbjdj7esmfmoebbcks4
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