Classification With the Sparse Group Lasso

Rao, Nikhil; Nowak, Robert; Cox, Christopher R.; Rogers, Timothy T.

doi:10.1109/tsp.2015.2488586

Cited by 67 publications

(39 citation statements)

References 24 publications

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“…The advantage of multi-task learning makes it suitable for multi-site data learning, considering the site as task, and the site-shared and site-specific features as task-shared and task-specific features. Neuroimaging studies have shown the effectiveness of performing multi-task learning in the brain decoding and disease classification ( Marquand et al, 2014 ; Obozinski et al, 2010 ; Rao et al, 2013 ; Wang et al, 2015 ; Watanabe et al, 2014 ). Specifically, multi-site fMRI data of ADHD was demonstrated better than single-site classification by learning site-shared and site-specific features using multi-task scheme ( Watanabe et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

Zhang

Zanetti

et al. 2018

NeuroImage: Clinical

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With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.

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Section: Introductionmentioning

confidence: 99%

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

Zhang

Zanetti

et al. 2018

NeuroImage: Clinical

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“…LASSO has gained wide spread popularity in signal processing and statistical learning, see [42], [43], [44]. LASSO has also been applied to forecast electricity price [20], [45], but its application to load forecasting is still a new topic.…”

Section: Sparsity In Autoregressive Modelsmentioning

confidence: 99%

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

Zhang

Weng

et al. 2017

IEEE Trans. Power Syst.

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Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance. Index TermsLoad forecasting, least absolute shrinkage and selection, sparse autoregressive model, significance test NOMENCLATURE y t Consumption at time t.

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“…Simon et al (2013) developed sparse GL (SGL) that uses the ℓ 2 penalty to select only a subset of the groups and the ℓ 1 penalty to select only a subset of the variables within the group. Indeed, SGL has been widely applied in detecting genetic variants (Rao et al, 2015;Li et al, 2017;Samal et al, 2017;Guo et al, 2019). Samal et al (2017) proposed a method based on SGL to identify phenotype associated extreme currents decomposed from metabolic networks data.…”

Section: Introductionmentioning

confidence: 99%

Genetic Variants Detection Based on Weighted Sparse Group Lasso

et al. 2020

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Identification of genetic variants associated with complex traits is a critical step for improving plant resistance and breeding. Although the majority of existing methods for variants detection have good predictive performance in the average case, they can not precisely identify the variants present in a small number of target genes. In this paper, we propose a weighted sparse group lasso (WSGL) method to select both common and lowfrequency variants in groups. Under the biologically realistic assumption that complex traits are influenced by a few single loci in a small number of genes, our method involves a sparse group lasso approach to simultaneously select associated groups along with the loci within each group. To increase the probability of selecting out low-frequency variants, biological prior information is introduced in the model by re-weighting lasso regularization based on weights calculated from input data. Experimental results from both simulation and real data of single nucleotide polymorphisms (SNPs) associated with Arabidopsis flowering traits demonstrate the superiority of WSGL over other competitive approaches for genetic variants detection.

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Classification With the Sparse Group Lasso

Cited by 67 publications

References 24 publications

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

Genetic Variants Detection Based on Weighted Sparse Group Lasso

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