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Fused Lasso for Feature Selection using Structural Information [article]

Lu Bai, Lixin Cui, Yue Wang, Philip S. Yu, Edwin R. Hancock
2019 arXiv   pre-print
However, most state-of-the-art methods tend to overlook the structural correlation information between pairwise samples, which may encapsulate useful information for refining the performance of feature  ...  To overcome these issues, we propose a new feature selection method using structural correlation between pairwise samples.  ...  Therefore, we unify the minimization problem of fused lasso and Eq.(5) and propose the so called fused lasso for feature selection using structural information(InFusedLasso), which is mathematically formulated  ... 
arXiv:1902.09947v3 fatcat:gw6z32etmndwheic7ght3ab4oy

Structure-Leveraged Methods in Breast Cancer Risk Prediction

Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside
2016 Journal of machine learning research  
Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these  ...  The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records.  ...  We thank the anonymous reviewers for their valuable comments and suggestions.  ... 
pmid:28559747 pmcid:PMC5446896 fatcat:hagsnxlvtvabrgmexnignzlt4q

Integrative regression network for genomic association study

Reddy Rani Vangimalla, Hyun-hwan Jeong, Kyung-Ah Sohn
2016 BMC Medical Genomics  
In particular, association studies of gene expression traits with respect to multi-layered genomic features are highly useful for uncovering the underlying mechanism.  ...  Our method facilitates identification of the strong signals as well as weaker signals by fusing information from different regression techniques.  ...  while Lasso, which uses no structural information, produces the largest MSE.  ... 
doi:10.1186/s12920-016-0192-7 pmid:27535739 pmcid:PMC4989890 fatcat:ht5ufxzfezbgfkhuj2ref5fv5y

Sparse methods for biomedical data

Jieping Ye, Jun Liu
2012 SIGKDD Explorations  
They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging.  ...  For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway  ...  For example, in the study of arrayCGH [99; 100] , the features-the DNA copy numbers along the genome-have the natural spatial order, and the fused Lasso, which incorporates the structure information using  ... 
doi:10.1145/2408736.2408739 pmid:24076585 pmcid:PMC3783968 fatcat:z4axej6w6vfmbmgg72spx2neya

Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm

Chanhee Park, Seoung Bum Kim
2016 Journal of Process Control  
Furthermore, the fused lasso is especially useful for analyzing highdimensional data in which the features exhibit a natural order, as is the case in spectroscopic signals.  ...  The results showed that the VM model constructed with features selected by the fused lasso algorithm yields more accurate and robust predictions than the lasso-and elastic net-based VM models.  ...  Acknowledgments We thank the editor and referees for their constructive comments and suggestions, which greatly improved the quality of the paper. This work was supported by the BK21 Plus and  ... 
doi:10.1016/j.jprocont.2016.04.002 fatcat:cr6wbf5ezbbupac2ngwcbkecli

Incorporating Prior Information with Fused Sparse Group Lasso: Application to Prediction of Clinical Measures from Neuroimages [article]

Joanne C. Beer, Howard J. Aizenstein, Stewart J. Anderson, Robert T. Krafty
2018 arXiv   pre-print
We propose using the fused sparse group lasso penalty to encourage structured, sparse, interpretable solutions by incorporating prior information about spatial and group structure among voxels.  ...  We present optimization steps for fused sparse group lasso penalized regression using the alternating direction method of multipliers algorithm.  ...  Feature selection was then performed using a treestructured group lasso penalty, and the selected features were used in a linear SVM to discriminate Alzheimer's disease patients from healthy controls.  ... 
arXiv:1801.06594v3 fatcat:lxmwcqro3nbbdod64eihjh5fcm

Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine [article]

Takanori Watanabe, Daniel Kessler, Clayton Scott, Michael Angstadt, Chandra Sripada
2014 arXiv   pre-print
Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection.  ...  Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes.  ...  Welsh, University of Michigan, for providing us with ConnTool, a functional connectivity analysis package.  ... 
arXiv:1310.5415v2 fatcat:iv5w3cs3ofbcjcsuptl3biedve

Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine

Takanori Watanabe, Daniel Kessler, Clayton Scott, Michael Angstadt, Chandra Sripada
2014 NeuroImage  
Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection.  ...  Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes.  ...  Welsh, University of Michigan, for providing us with ConnTool, a functional connectivity analysis package.  ... 
doi:10.1016/j.neuroimage.2014.03.067 pmid:24704268 pmcid:PMC4072532 fatcat:n35ycrdfrrhn5p2ml7cp4s7q3u

Effective Feature Selection for Feature Possessing Group Structure

Yasmeen Sheikh
2017 International Journal Of Engineering And Computer Science  
The underlying structure has been ignored by the previous feature selection method and it determines the feature individually.  ...  Considering this we focus on the problem where feature possess some group structure. To solve this problem we present group feature selection method at group level to execute feature selection.  ...  FEATURE SELECTION METHODS The feature selection method is divided into three category based on their label information and label information method is used most commonly used.  ... 
doi:10.18535/ijecs/v6i5.19 fatcat:ebcldr5rtvcrtbz76a4loy2bky

A Disentangled Representation Based Brain Image Fusion via Group Lasso Penalty

Anqi Wang, Xiaoqing Luo, Zhancheng Zhang, Xiao-Jun Wu
2022 Frontiers in Neuroscience  
Then, based on the disentangled representations, different fusion strategies are adopted for complementary features and redundant features, respectively.  ...  Especially, to promote the disentanglement of complement and redundancy, a complementary group lasso penalty is proposed to constrain the extracted feature maps.  ...  Considering the redundancy may exist among features, proposed using Group lasso to prevent the selection of redundant features which may have high correlations with other features.  ... 
doi:10.3389/fnins.2022.937861 pmid:35924221 pmcid:PMC9340788 fatcat:2sfoihq5uzbgreov2ytcoprtge

Feature selection guided by structural information

Martin Slawski, Wolfgang zu Castell, Gerhard Tutz
2010 Annals of Applied Statistics  
The generalized ridge-type constraint will typically make use of the known association structure of features, for example, by using temporal- or spatial closeness.  ...  In this vein, we provide an analog to the so-called "irrepresentable condition" which holds for the lasso.  ...  SUPPLEMENTARY MATERIAL Supplement to "Feature Selection guided by Structural Information" (DOI: 10.1214/09-AOAS302SUPP; .pdf). The supplement contains proof of all statements of the main article.  ... 
doi:10.1214/09-aoas302 fatcat:vvvpshhienhdnjohwmiqpt56u4

Sparse Group Selection on Fused Lasso Components for Identifying Group-Specific DNA Copy Number Variations

Ze Tian, Huanan Zhang, Rui Kuang
2012 2012 IEEE 12th International Conference on Data Mining  
Assuming a given group structure on patient samples by clinical information, sparse group selection on fused lasso (SGS-FL) identifies the optimal latent CNV components, each of which is specific to the  ...  In this paper, we propose a latent feature model that couples sparse sample group selection with fused lasso on CNV components to identify group-specific CNVs.  ...  The authors gratefully thank Christina Leslie and Jieping Ye for helpful discussions.  ... 
doi:10.1109/icdm.2012.35 dblp:conf/icdm/TianZK12 fatcat:pri5hzjoubgp3dcgg7ok4ennfa

ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso [article]

Weijian Luo, Yongxian Long
2020 arXiv   pre-print
some noisy variance in continous variable range and keep useful leveled information with good ordered encodings.However, to our best knowledge a majority of cutting point selection is done via researchers  ...  We firstly cut each variable range into fine grid bins and train model with our group and group fused lasso regularization on each successive bins.It is a method that integrates feature engineering,variable  ...  give a theoretical analysis for group and fused lasso method.  ... 
arXiv:2009.10498v1 fatcat:4zs6zcfukfewtol6r7pknpxfmq

Relational Lasso - An Improved Method Using the Relations Among Features -

Kotaro Kitagawa, Kumiko Tanaka-Ishii
2011 International Joint Conference on Natural Language Processing  
By using automatically obtained noisy relations among features, relational lasso learns an additional penalty parameter per feature, which is then incorporated in terms of a regularizer within the target  ...  Relational lasso is a method that incorporates feature relations within machine learning.  ...  (Yuan and Lin, 2006) proposed grouped lasso, which incorporates underlying groups among features. The fused and grouped lasso methods require configuration of the structure among features.  ... 
dblp:conf/ijcnlp/KitagawaT11 fatcat:6uulgshh55dmjfyh76wigqivya

Modeling disease progression via fused sparse group lasso

Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, Jieping Ye
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty.  ...  Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers  ...  Note that in the above stability selection we use temporal information via fused Lasso.  ... 
doi:10.1145/2339530.2339702 pmid:25309808 pmcid:PMC4191837 dblp:conf/kdd/ZhouLNY12 fatcat:quicxpfhpzbvvosjmiqsbsudzq
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