Filters








7,835 Hits in 5.2 sec

Discriminative Elastic-Net Regularized Linear Regression

Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
2017 IEEE Transactions on Image Processing  
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

Kumud Arora, Inderprastha Engineering College, Ghaziabad, India, Poonam Garg
2020 International Journal of Machine Learning and Computing  
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

Bhanukiran Vinzamuri, Chandan K. Reddy
2013 2013 IEEE 13th International Conference on Data Mining  
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

Jiming Li, Yuntao Qian
2011 2011 IEEE International Geoscience and Remote Sensing Symposium  
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]

Bernard Ng, Arash Vahdat, Ghassan Hamarneh, Rafeef Abugharbieh
2010 Lecture Notes in Computer Science  
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

Stefan J. Teipel, Michel J. Grothe, Coraline D. Metzger, Timo Grimmer, Christian Sorg, Michael Ewers, Nicolai Franzmeier, Eva Meisenzahl, Stefan Klöppel, Viola Borchardt, Martin Walter, Martin Dyrba
2017 Frontiers in Aging Neuroscience  
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

Bhanukiran Vinzamuri, Yan Li, Chandan K. Reddy
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
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

Ali Alameer, Patrick Degenaar, Kianoush Nazarpour
2016 2016 International Conference for Students on Applied Engineering (ISCAE)  
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]

Shaode Yu, Haobo Chen, Hang Yu, Zhicheng Zhang, Xiaokun Liang, Wenjian Qin, Yaoqin Xie, Ping Shi
2020 arXiv   pre-print
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

Tahereh Mohammadi Majd, Shiva Kalantari, Hadi Raeisi Shahraki, Mohsen Nafar, Afshin Almasi, Shiva Samavat, Mahmoud Parvin, Amirhossein Hashemian
2018 Iranian Biomedical Journal  
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]

Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Goldstein, Matthew B. Blaschko
2013 Lecture Notes in Computer Science  
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

Tahereh Mohammadi Majd, Shiva Kalantari, Hadi Raeisi Shahraki, Mohsen Nafar, Afshin Almasi, Shiva Samavat, Mahmoud Parvin, Amirhossein Hashemian, Department of Biostatistics and Epidemiology, Kermanshah University of Medical Sciences, School of Public Health, Kermanshah, Iran, Chronic Kidney Disease Research Center, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran, Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran, Urology-Nephrology Research Center, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran (+4 others)
2018 Iranian Biomedical Journal  
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

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, B. Thirion
2011 IEEE Transactions on Medical Imaging  
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

Vincent Michel, Alexandre Gramfort, Gael Varoquaux, Bertrand Thirion
2010 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging  
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]

Yilun Wang, Zhiqiang Li, Yifeng Wang, Xiaona Wang, Junjie Zheng, Xujuan Duan, Huafu Chen
2016 arXiv   pre-print
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
« Previous Showing results 1 — 15 out of 7,835 results