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Recent works have shown that the resting-state brain functional connectivity hypernetwork, where multiple nodes can be connected, are an effective technique for brain disease diagnosis and classification research. The lasso method was used to construct hypernetworks by solving sparse linear regression models in previous research. But, constructing a hypernetwork based on the lasso method simply selects a single variable, in that it lacks the ability to interpret the grouping effect. Considering the group structure problem, the previous study proposed to create a hypernetwork based on the elastic net and the group lasso methods, and the results showed that the former method had the best classification performance. However, the highly correlated variables selected by the elastic net method were not necessarily in the active set in the group. Therefore, we extended our research to address this issue. Herein, we propose a new method that introduces the sparse group lasso method to improve the construction of the hypernetwork by solving the group structure problem of the brain regions. We used the traditional lasso, group lasso method, and sparse group lasso method to construct a hypernetwork in patients with depression and normal subjects. Meanwhile, other clustering coefficients (clustering coefficients based on pairs of nodes) were also introduced to extract features with traditional clustering coefficients. Two types of features with significant differences obtained after feature selection were subjected to multi-kernel learning for feature fusion and classification using each method, respectively. The network topology results revealed differences among the three networks, where hypernetwork using the lasso method was the strictest; the group lasso, most lenient; and the sgLasso method, moderate. The network topology of the sparse group lasso method was similar to that of the group lasso method but different from the lasso method. The classification results show that the sparse group lasso method achieves the best classification accuracy by using multi-kernel learning, which indicates that better classification performance can be achieved when the group structure exists and is properly extended.
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1Feature level-based group lasso method for amnestic mild cognitive impairment diagnosis
Feature level-based group lasso method for amnestic mild cognitive impairment diagnosis
23Prediction of internal egg quality characteristics and variable selection using regularization methods: ridge, LASSO and elastic net
Abstract. This study was conducted to determine the inner quality characteristics of eggs using external egg quality characteristics. The variables were selected in order to obtain the simplest model using ridge, LASSO and elastic net regularization methods. For this purpose, measurements of the internal and external characteristics of 117 Japanese quail eggs were made. Internal quality characteristics were egg yolk weight and albumen weight; external quality characteristics were egg width, egg length, egg weight, shape index and shell weight. An ordinary least square method was applied to the data. Ridge, LASSO and elastic net regularization methods were performed to remove the multicollinearity of the data. The regression estimating equations of the internal egg quality were significant for all methods (P<0.01). The goodness of fit of the regression estimating equations for egg yolk weight was 58.34, 59.17 and 59.11 % for the ridge, LASSO and elastic net methods, respectively. For egg albumen weight the goodness of fit of the regression estimating equations was 75.60 %, 75.94 % and 75.81 % for the respective ridge, LASSO and elastic net methods. It was revealed that LASSO, including two predictors for both egg yolk weight and egg albumen weight, was the best model with regard to high predictive accuracy.
Read moreStructure discovery and parametrically guided regression
In regression analysis, a parametric model is often assumed from prior information or a pilot study. If the model assumption is valid, the parametric method is useful. However, the efficiency of the estimator is not guaranteed when a poor model is selected. This article aims to check whether the model assumption is correct or not and to estimate the regression function. To achieve this, we propose a hybrid technique of parametrically guided method and group lasso. First, the parametric model is prepared. The parametrically guided estimator is constructed by summing the parametric estimator and nonparametric estimator. For the estimation of the nonparametric component, we use B‐splines and the group lasso method. If the nonparametric component is estimated to be a zero function, the parametrically guided estimator is reduced to the parametric estimator. Then, we can decide that the parametric model assumption is correct. If the nonparametric estimator is not zero, the semiparametric estimator is obtained. Thus, the proposed method discovers the model structure and estimates the regression function simultaneously. We investigate the asymptotic properties of the proposed estimator. A simulation study and real data example are presented. Copyright © 2016 John Wiley & Sons, Ltd.
Read more14Jointly Sparse Signal Recovery via Deep Auto-encoder and Parallel Coordinate Descent Unrolling
In this paper, combining techniques in compressed sensing, parallel optimization and deep learning, an autoencoder-based approach is proposed to jointly design the common measurement matrix and jointly sparse signal recovery method for complex sparse signals. The encoder achieves noisy linear compression for jointly sparse signals, with a common measurement matrix. The decoder realizes jointly sparse signal recovery based on an iterative parallel-coordinate descent algorithm which is proposed to solve GROUP LASSO in a parallel manner. In particular, the decoder consists of an approximation part which unfolds (several iterations of) the proposed iterative algorithm to obtain an approximate solution of GROUP LASSO and a correction part which reduces the difference between the approximate solution and the actual jointly sparse signals. To our knowledge, this is the first time that an optimization-based jointly sparse signal recovery method is implemented using a neural network. The proposed approach achieves higher recovery accuracy with less computation time than the classic GROUP LASSO method, and the gain significantly increases in the presence of extra structures in sparse patterns. The common measurement matrix obtained by the proposed approach is also suitable for the classic GROUP LASSO method. We consider an application example, i.e., channel estimation in Multiple-Input Multiple-Output (MIMO)-based grant-free massive access for massive machine-type communications (mMTC). By numerical results, we demonstrate the substantial gains of the proposed approach over GROUP LASSO and AMP when the number of jointly sparse signals is not very large.
Read moreUnveiling regional drivers of the blue economy in Sumatra: A multiscale space–time regression analysis with variable selection
Indonesia, as a maritime country, positions the blue economy as a cornerstone in the 2025–2045 National Long-Term Development Plan (RPJPN) to support sustainable economic growth, particularly through the development of the fisheries sector and marine tourism. This study aims to construct a Blue Economy Index (BEI) at the district/city level and identify its key determinants using the Regression Weighted Geographic and Temporal Multiscale (MGTWR) method. The BEI is based on environmental, social, and economic dimensions using data from 2020 to 2022 across 154 districts/cities on Sumatra Island. The analysis incorporates 14 predictor variables grouped into four dimensions and selected through Group LASSO and Elastic Net methods. The best-performing model is the RTGTM with Group LASSO variable selection and a Gaussian kernel function, yielding an of 30.71% and an AIC of 1377.52. The social dimension contributes most to the BEI, while Medan City and Subulussalam City consistently record the highest and lowest scores. Significant variables influencing BEI include population factors (number of sub-districts, total population, sex ratio), education factors (number of senior high schools and vocational schools), and environmental indicators (protected drinking water sources and sanitation access). These findings underscore the importance of multi-dimensional, spatial, and temporal approaches in evaluating and advancing blue economy policies at the regional level.
Read more19Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer
High-grade serous ovarian cancer (HGSC) is an aggressive cancer with a worse clinical outcome. Therefore, studies about the prognosis of HGSC may provide therapeutic avenues to improve patient outcomes. Since genome alteration are manifested at the protein level, we integrated protein and mRNA data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and utilized the sparse overlapping group lasso (SOGL) method, a new mechanism-driven variable selection method, to select dysregulated pathways and crucial proteins related to the survival of HGSC. We found that biosynthesis of amino acids was the main biological pathway with the best predictive performance (AUC = 0.900). A panel of three proteins, namely EIF2B1, PRPS1L1 and MAPK13 were selected as potential predictive proteins and the risk score consisting of these three proteins has predictive performance for overall survival (OS) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively. Our study provides additional information for further mechanism and therapeutic avenues to improve patient outcomes in clinical practice.
Read more7Genetic Variants Detection Based on Weighted Sparse Group Lasso
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 low-frequency 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.
Read more291Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?
Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?
26Sparse EEG/MEG source estimation via a group lasso
Non-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches.
Read moreLearning to Rank for Blind Image Quality Assessment
We propose a blind image quality assessment (BIQA) model using learning to rank (LTR) method in order to overcome the defects of the existing BIQA model. In this model, we first simplify the problem of predicting exact image quality score with the classification of image quality preference and we then use feature fusion and multiple kernel learning based on group lasso (MKLGL) methods to train a classifier. Finally, based on the voting strategy, a simple and effective image quality prediction model is constructed. Experimental results on the existing IQA datasets show that our proposed method is highly consistent with the subjective perception of human eyes and is superior to existing BIQA methods. In addition, our method is highly scalable.
Read more11Phase I outlier detection in profiles with binary data based on penalized likelihood
Profile monitoring has been proven to be important among statistical process control problems. In some specific applications, the response variable of a profile can be categorical data, and numerous methods have been proposed for monitoring this type of profiles. Nevertheless, outlier detection for profiles with categorical data still attracted insufficient attention in the literature. To this end, this paper focuses on binary profiles and develops two schemes for outlier detection, from the viewpoint of penalized likelihood, based on the group LASSO method and directional information, respectively, in which the profiles of interest are treated as an integrated high‐dimensional vector. Simulation study shows that the proposed group‐type scheme usually performs better than the existingchart in terms of detecting outliers correctly and alleviating the masking effect. Finally, a real example is used for illustrating the implementation of the proposed scheme.
Read more150Consistent group selection in high-dimensional linear regression
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive method for variable selection since it respects the grouping structure in the data. We study the selection and estimation properties of the group Lasso in high-dimensional settings when the number of groups exceeds the sample size. We provide sufficient conditions under which the group Lasso selects a model whose dimension is comparable with the underlying model with high probability and is estimation consistent. However, the group Lasso is, in general, not selection consistent and also tends to select groups that are not important in the model. To improve the selection results, we propose an adaptive group Lasso method which is a generalization of the adaptive Lasso and requires an initial estimator. We show that the adaptive group Lasso is consistent in group selection under certain conditions if the group Lasso is used as the initial estimator.
Read moreA Multi-Kernel Embedding Fusion Framework for Physiological Signal Based Emotion Recognition
Physiological signal-based emotion recognition requires effective fusion of multi-modal physiological signals to improve recognition accuracy. In this paper, a multi-kernel embedding fusion framework (MKEFF) is proposed for multi-modal physiological signal emotion recognition. Specifically, multi-kernel learning and kernel approximation techniques are used to compute the multi-kernel embeddings of the original feature vectors of each modality independently. The embeddings are then fed in parallel to their respective representation learning layer, where the proposed sparse relation learning method is applied to all the modalities to explore the correlation and diversity among them. Finally, a distribution alignment based fusion method is proposed to align each modality in the subspace, and a weighted summation fusion is performed to obtain the fused representations. Extensive cross-subject emotion recognition experiments are conducted on three public datasets, DEAP, DECAF, and SEED-IV, to evaluate the proposed method. The experimental results demonstrate that the proposed method achieves better classification performance and interpretability than the state-of-the-art methods.
Read moreDifferentiation between suicide attempt and suicidal ideation in patients with major depressive disorder using cortical functional network
IntroductionThe detection of electrophysiological markers to differentiate patients with suicide attempts could support the efforts of clinicians to provide proper diagnosis and treatment for them, and finally reduce the suicide mortality rate.ObjectivesThe objective of the current study was to investigate and compare cortical functional networks from resting-state electroencephalogram in patients with suicide attempts and suicidal ideation using source-level weighted network analysis. We also examined the possibility of network features serving as biomarkers by differentiation between suicide attempts and suicidal ideation using machine learning techniques.MethodsThis study investigated source-level cortical functional networks using resting-state electroencephalography in drug-naïve depressed patients with suicide attempt and suicidal ideation. Electroencephalogram was recorded in 55 patients with suicide attempts and in 54 patients with suicidal ideation. Graph-theory-based source-level weighted functional networks were assessed using strength, clustering coefficient (CC), and path length (PL) in seven frequency bands. Finally, we applied machine learning to differentiate between the two groups using source-level network features.ResultsThere were significant differences in the three global level indices of the high alpha band. The strength and CC of the high alpha band were significantly lower in patients with suicide attempts than in those with suicide attempts ideation. In contrast, the PL of the high alpha band was significantly higher in patients with suicide attempts than in those with suicide ideation. No significant differences in the other frequency bands were between the two groups.The nodal CCs of patients with suicide attempts were significantly lower than those of patients with suicide ideation in most regions, except for three of the 68 regions. The best classification performance between suicide attempts and suicide ideation was an accuracy of 73.39%, a sensitivity of 76.36%, and a specificity of 70.37% with 17 features. Three of these features were global-level network indices (strength, CC, and PL) in the high alpha band; the other 14 features were high alpha band nodal level CCs in limbic, frontal, temporal, parietal,and occipital area.Image 1:Image 2:Image 3:ConclusionsThis is the first study to use electroencephalogram source level network measures, revealing that network indices for high alpha band could be potential biomarkers to distinguish betweensuicide attempts and suicide ideation in patients with depression. Moreover, our study evaluated the electroencephalogram signals immediately after fatal suicide attempts in a relatively large number of un-medicated patients.Disclosure of InterestNone Declared
Read moreGlobal Variable Selection for Quantile Regression
Quantile regression provides a parsimonious model for the conditional quantile function of the response variable Y given the vector of covariates X, and describes the whole conditional distribution of the response, yielding estimators that are more robust to the presence of outliers. Quantile regression models specify, for each quantile level τ , the functional form for the conditional τ -th quantile of the response, which brings complexity to perform variable selection using regularization techniques, such as LASSO or adaptive LASSO (adaLASSO), as one might obtain a different set of selected variables for each quantile level. In this work, we propose a method for global variable selection and coefficient estimation in the linear quantile regression framework, imposing few restrictions on the functional form of β(·), and applying group adaLASSO penalization for variable selection. We set up a Monte Carlo study comparing six different proposed estimators based on LASSO, adaLASSO and group LASSO in six scenarios that diversify sample and quantile levels grid sizes. The findings demonstrate that the selection of the tuning parameter λ for penalization is critical for model selection and coefficient estimation. It was observed that the methods using traditional LASSO are more prone to include the true model as compared to adaLASSO, but renouncing model shrinkage and not removing irrelevant covariates, while the grouped approaches are more effective in zeroing coefficients that are less relevant.
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