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IEEE International Conference on Image Processing (ICIP)

作者： Madine, Mohammad Rekik, Islem Werghi, Naoufel Khalifa Univ Ctr Cyber Phys Syst Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Istanbul Tech Univ Fac Comp & Informat Engn BASIRA Lab Istanbul Turkey

ISBN: (纸本)9781728163956

The field of network neuroscience provided unprecedented insights into how brain connectivity gets altered by autism spectrum disorder (ASD) on functional, structural, and morphological levels. However, a few studies have looked to design a framework that captures the complex network structure of the brain and disentangles the heterogeneity of ASD. In this paper, we leverage multi-kernel unsupervised learning in the construction of multiview hypergraph neural networks (HGNN), each capturing a particular view of the brain connectome, to eventually distinguish between ASD and normal control (NC) subjects. Additionally, we tested and measured how our proposed framework compares to other variants based on previous baseline methods. Our classification results outperformed comparison methods and agreed with the literature in the sense that the right hemisphere connectivity was more discriminative in ASD diagnosis than the left hemisphere.

关键词： autism spectrum disorder hypergraph neural network multi-kernel learning

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Hand motion recognition via multi-kernel manifold learning

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-11页

作者： Li, Xiangzhe Wang, Sheng Zhang, Yuanpeng Wu, Qinfeng Nanjing Med Univ Rehabil Med Ctr Affiliated Suzhou Sci & Technol Town Hosp Suzhou Peoples R China Hong Kong Polytech Univ Deparment Hlth Technol & Informat Hong Kong Peoples R China

In the context of smart city, hand motion recognition has once again become a research hotspot, especially the surface electromyographic (sEMG) based hand motion recognition. When sEMG signals contain noise, the overfitting problem always exists. To solve this problem, a multi-kernel label relaxation ridge regression with manifold embedding is proposed. Firstly, a non-negative matrix is introduced to relax the label matrix of the training data, so that the proposed model has more freedom in label fitting. At the same time, label relaxation can enlarge the margins between classes as much as possible. Secondly, based on the idea of manifold learning, the compactness graph is introduced to ensure that the training data in the same class should be as close as possible in the conversion space when they are converted from the original feature space to the label space so as to improve the generalization ability. Finally, 15 UCI datasets and the hand motion dataset Ninapro are used to verify the proposed model. The experimental results show that the proposed model has certain advantages over comparative ones in terms of classification accuracy and generalization ability.

关键词： Smart city multi-kernel learning Label relaxation Manifold learning Compactness graph Ridge regression

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A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and multi-kernel Extreme learning Machine

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JOURNAL OF NEUROSCIENCE METHODS 2024年 407卷 110136页

作者： Guan, Shan Cong, Longkun Wang, Fuwang Dong, Tingrui Northeast Elect Power Univ Sch Mech Engn Jilin 132012 Jilin Province Peoples R China

Background: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. New method: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and multi-kernel Extreme learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using multi-kernel Extreme learning Machine (MKELM). Results: After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. Comparison with other methods and conclusions: We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Int

关键词： Single-joint Motor imagery Empirical wavelet multi-kernel learning Extreme learning machine

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Fully-Decentralized multi-kernel Online learning over Networks

Fully-Decentralized Multi-Kernel Online Learning over Networ...

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IEEE Global Communications Conference (GLOBECOM)

作者： Chae, Jeongmin Mitra, Urbashi Hong, Songnam Univ Southern Calif Elect & Comp Engn Los Angeles CA 90007 USA Hanyang Univ Elect Engn Seoul South Korea

ISBN: (纸本)9781728181042

Fully decentralized online learning with multiple kernels (named FDOMKL) is studied, where each node in a network learns a sequence of global functions in an online fashion without the control of a central server. Every node finds the best global function only using information from its one-hop neighboring nodes via online alternating direction method of multipliers (ADMM) and the network-wise Hedge algorithm. The learning framework for an individual node is based on kernel learning and the proposed algorithm successfully harness multi-kernel method to find the best common function over the entire network. To the best of our knowledge, this is the first work that proposes a fully-decentralized online learning algorithm based on multiple kernels. The proposed FDOMKL preserves privacy by maintaining the local data at the edge nodes and exchanging model parameters only. We prove that FDOMKL achieves a sublinear regret bound compared with the best kernel function in hindsight under certain assumptions. In addition, numerical tests on real time-series datasets demonstrate the superiority of the proposed algorithm in terms of learning accuracy and network consistency compared to state-of-the-art single kernel methods.

关键词： Decentralized learning online learning multi-kernel learning online convex optimization

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multi-kernel learning based Autonomous Fault Diagnosis for Centrifugal Pumps 7

Multi-kernel Learning based Autonomous Fault Diagnosis for C...

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7th International Conference on Control Automation and Information Sciences (ICCAIS)

作者： Feng, Haoran Wang, Yongcai Zhu, Jiajun Li, Deying Peking Univ Joint Lab Natl Engn Res Ctr Software Engn & Hanpu Beijing 100871 Peoples R China Renmin Univ China Dept Comp Sci Beijing 100872 Peoples R China

ISBN: (纸本)9781538660201

Centrifugal pumps are fundamental instruments in many industry plants, whose continuously operation plays an important role in the production cycle. For improving production efficiency, autonomous pump fault diagnosis has been widely adopted by many enterprises and has also attracted great research attentions. Existing studies exploit machine learning algorithms for autonomous pump fault diagnosis, which generally needs human knowledge to select distinctive data features. To avoid the bias of human selection, this paper proposes a multi kernel learning (NIKE) based autonomous pump fault diagnosis method. It trains basic classifiers (BCs) by each feature and weightily combines the basic classifiers to form the combined classifier for fault diagnosis. An autonomous BC weighting algorithm is proposed, which trains the combination weights of the BCs autonomously. We show the NIKE based fault diagnosis method provide more accurate fault detection than the existing methods and without the need of human experts' interventions.

关键词： multi-kernel learning Centrifugal Pump Autonomous Fault Diagnosis

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Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning

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ENERGY CONVERSION AND MANAGEMENT 2018年 163卷 384-406页

作者： Wang, Yun Xie, Zongxia Hu, Qinghua Xiong, Shenghua Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China Tianjin Univ Dept Management & Econ Tianjin Peoples R China

For the management of wind energy, wind speed forecasting is often required. Accurate multi-step ahead wind speed forecasts make the power system be adjusted timely and properly to ensure the stable and efficient operation of power system. Currently, various techniques have been developed for multi-step ahead wind speed forecasting. However, the correlation among different forecasting steps is often neglected in current multi-step ahead wind speed forecasting approaches, and the characteristic of heteroscedasticity in wind speed forecasting errors is usually not taken into consideration. In this work, a novel multi-step ahead forecasting method based on multi-kernel learning is developed. This method considers the task correlation, which represented by the covariance of multi-step ahead forecasting tasks, as well as the heteroscedasticity of forecasting errors. The optimization is solved within the framework of variational Bayesian. Thus, a correlation aware multi-step ahead wind speed forecasting technique with heteroscedastic multi-kernel learning is designed. In this paper, the experimental results in different wind farms and different seasons prove that the regression model considering the characteristics of multi-step ahead wind speed forecasting, task correlation and heteroscedasticity, will produce more accurate forecasts than the other models as for two to six-ahead wind speed forecasting. However, it is difficult to tell which characteristic is more important from the forecasting results. So, the regression model considering both of them will be more reasonable. Moreover, the training time of the proposed model is more than 10 min but less than 20 min. Thus, two to six-step ahead wind speed forecasts can be used in some practical applications, such as the load dispatch planning and the load increment/decrement decisions.

关键词： multi-step ahead wind speed forecasting Heteroscedasticity Task correlation multi-kernel learning Matrix-variate Gaussian Variational Bayesian

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MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning

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multiMEDIA TOOLS AND APPLICATIONS 2018年第22期77卷 29651-29667页

作者： Liu, Jin Wang, Xiang Zhang, Xiangrong Pan, Yi Wang, Xiaosheng Wang, Jianxin Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Cent South Univ Inst Med Psychol Xiangya Hosp 2 Changsha 410011 Hunan Peoples R China Nanjing Med Univ Dept Geriatr Psychiat Affiliated Nanjing Brain Hosp Nanjing 210029 Jiangsu Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA Cent South Univ Dept Human Anat & Neurobiol Xiangya Sch Med Changsha 410013 Hunan Peoples R China

Schizophrenia (SZ) is a complex neuropsychiatric disorder that seriously affects the daily life of patients. Therefore, accurate diagnosis of SZ is essential for patient care. Several T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) markers (e.g., cortical thickness (CT), mean diffusivity (MD)) for SZ have been identified by using some existing brain atlases, and have been used successfully to discriminate patients with SZ from healthy controls (HCs). Currently, these markers have mainly been used separately. Thus, the full potential of T1-weighted MRI images and DTI images for SZ diagnosis might not yet have been used comprehensively. Furthermore, the extraction of these markers based on single brain atlas might not yet be able to use the full potential of these images. Therefore, in this study, we propose a multi-modality multi-atlas feature representation and a multi-kernel learning method (MMM) to perform SZ/HC classification. Firstly, we extract 8 feature sets from T1-weighted MRI images and DTI images via 4 existing brain atlases and 4 markers. Then, a two-step feature selection method is proposed to select the most discriminative features of each feature set for SZ/HC classification. Finally, a multiple feature sets based multi-kernel SVM learning method (MFMK-SVM) is proposed to combine all feature sets for SZ/HC classification. Experimental results show that our proposed method achieves an accuracy of 91.28%, a sensitivity of 90.85%, a specificity of 92.17% and an AUC of 0.9485 for SZ/HC classification. Experimental results illustrate that our proposed classification method is efficient and promising for clinical diagnosis of SZ.

关键词： Schizophrenia multi-modality multi-atlas multi-kernel learning Classification

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multi-kernel learning for multivariate performance measures optimization

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NEURAL COMPUTING & APPLICATIONS 2017年第8期28卷 2075-2087页

作者： Lin, Fan Wang, Jingbin Zhang, Nian Xiahou, Jianbing McDonald, Nancy Xiamen Univ Software Sch Xiamen 361005 Peoples R China Chinese Acad Sci Natl Time Serv Ctr Xian 710600 Peoples R China Chinese Acad Sci Grad Univ Beijing 100039 Peoples R China Xiamen Univ Technol Xiamen 361024 Peoples R China Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

In this paper, we investigate the problem of optimizing complex multivariate performance measures to learn classifiers for pattern classification problems. For the first time, the multi-kernel learning is considered to construct a classifier to optimize a given nonlinear and non-smooth multivariate classifier performance measure. We estimate and optimize the upper bound of the given multivariate performance measure, instead of optimizing it directly. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering minimizing the upper bound of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.

关键词： Pattern classification multivariate performance measures multi-kernel learning Alternate optimization Cutting plane algorithm

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ONLINE multi-kernel learning WITH ORTHOGONAL RANDOM FEATURES

ONLINE MULTI-KERNEL LEARNING WITH ORTHOGONAL RANDOM FEATURES

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shen, Yanning Chen, Tianyi Giannakis, Georgios B. Univ Minnesota Dept ECE Minneapolis MN 55455 USA Univ Minnesota DTC Minneapolis MN USA

ISBN: (纸本)9781538646588

kernel-based methods have well-appreciated performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. To cope with this limitation, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops an online multi-kernel learning scheme to infer the intended nonlinear function 'on the fly.' Performance analysis shows that the novel algorithm can afford sublinear regret. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.

关键词： multi-kernel learning random features online learning online regression

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A l_2,1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 140卷 211-231页

作者： Cao, Peng Liu, Xiaoli Zhang, Jian Li, Wei Zhao, Dazhe Huang, Min Zaiane, Osmar Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Minist Educ Key Lab Med Image Comp Shenyang Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Northeastern Univ Informat Sci & Engn Shenyang Peoples R China Univ Alberta Comp Sci Edmonton AB Canada

Objective: The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). Methods: In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a l(2,1) norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth l(2,1) regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the l(2,1) norm of kernel weights, and use an accelerated method based on FISTA;the second one employs an iterative scheme based on an approximate gradient descent method. Results: The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the l(2,1) norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. Conclusions: The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed l(2,1) norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

关键词： Lung nodule detection False positive reduction Classification Heterogeneous feature fusion multi-kernel learning

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