Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

Elastic Multiple Kernel Learning

(MKL ) 多重核学习被建议处理核熔化。MKL 听说线性联合几个核并且解决同时与联合的核联系的支持的向量机器(SVM ) 。MKL 的当前的框架鼓励核联合系数的稀少。核的重要部分什么时候是增进知识的，强迫稀少，趋于选择仅仅一些核并且可以忽略有用信息。在这份报纸，我们建议学习的有弹性的多重核(EMKL ) 完成适应的核熔化。EMKL 使用混合规则化功能损害稀少和非稀少。MKL 和 SVM 能被认为是 EMKL 的特殊情况。为 MKL 问题基于坡度降下算法，我们建议一个快算法解决 EMKL 问题。模拟数据集上的结果证明 EMKL 的表演有利地比作 MKL 和 SVM。我们进一步把 EMKL 用于基因集合分析并且得到有希望的结果。最后，我们学习比作另外的非稀少的 MKL 的 EMKL 的理论优点。

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

Incremental support vector machine algorithm based on multi-kernel learning

A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.

A stacked multiple kernel support vector machine for blast inducedflyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.

基于多核扩展卷积的无监督视频行人重识别

行人重识别旨在跨监控摄像头下检索出特定的行人目标。由于存在姿态变化、物体遮挡和背景干扰的不同成像条件等问题,导致行人特征提取不充分。本文提出一种利用多核扩展卷积的无监督视频行人重识别方法,使得提取到的行人特征能够更全面、更准确地表达个体差异和特征信息。首先,采用预训练的ResNet50作为编码器,为了进一步提升编码器的特征提取能力,引入了多核扩展卷积模块,通过增加卷积核的感受野,使得网络能够更有效地捕获到局部和全局的特征信息,从而更全面地描述行人的外貌特征;其次,通过解码器将高级语义信息还原为更为底层的特征表示,从而增强特征表示,提高系统在复杂成像条件下的性能;最后,在解码器的输出中引入多尺度特征融合模块融合相邻层中的特征,进一步减少不同特征通道层之间的语义差距,以产生更鲁棒的特征表示。在3个主流数据集上进行离线实验,结果表明该方法在准确性和鲁棒性上均取得了显著的改进。

基于Gram-Schmidt正交化和HSIC的核函数选择方法

核方法是一种解决非线性、异构数据的有效方法,核函数的选择问题是核方法中的一个重要课题,对于不同的应用问题,如何选择合适的核函数还没有足够的理论基础,不适当的核函数选取会降低核方法的性能。由此,提出了一种基于Gram-Schmidt正交化(GSO)和Hilbert-Schmidt独立准则的核选择方法(HSIC-GSO),该方法考虑了核函数选择过程中存在的不相关冗余信息。首先,利用GSO消除核函数之间的冗余信息;然后,使用HSIC度量核函数与理想核之间的相似性;最后,得到一组判别能力强、多样性大的基核函数。实验结果表明,HSIC-GSO方法选择的核函数泛化性好,并且提高了MKL的分类性能,验证了所提方法的有效性。

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.

Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning

Speech emotion recognition （SER） in noisy environment is a vital issue in artificial intelligence （AI）. In this paper, the reconstruction of speech samples removes the added noise. Acoustic features extracted from the reconstructed samples are selected to build an optimal feature subset with better emotional recognizability. A multiple-kernel （MK） support vector machine （SVM） classifier solved by semi-definite programming （SDP） is adopted in SER procedure. The proposed method in this paper is demonstrated on Berlin Database of Emotional Speech. Recognition accuracies of the original, noisy, and reconstructed samples classified by both single-kernel （SK） and MK classifiers are compared and analyzed. The experimental results show that the proposed method is effective and robust when noise exists.

EEG-based Emotion Recognition Using Multiple Kernel Learning

Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

基于测量阻抗动态轨迹的大型调相机失磁保护

大型调相机失磁故障严重影响设备本体安全以及电网稳定,现有基于静态阈值的低电压与无功反向判据可靠性与选择性不足。文中提出一种可反映调相机运行状态的机端测量阻抗全局动态轨迹智能识别的失磁保护原理,从运动学角度建立能够准确反映失磁与其他工况下测量阻抗轨迹的特征量时间序列,基于统计学提取解释性强的特征量。利用自适应权重的全局与局部核函数组合训练多核支持向量机(multiple kernel learning support vector machine,MKL-SVM),在保证模型学习能力的同时增强其泛化能力;提出基于分类核空间距离的两阶段识别策略,可在保证可靠性的前提下提高保护速动性。基于PSCAD仿真平台搭建调相机接入电网模型进行验证,结果表明所提失磁保护方案无须采集转子侧电气量,识别准确,面对新能源接入和未知扰动时仍具有优良的适用性。

基于E^2LSH-MKL的视觉语义概念检测

多核学习方法(Multiple kernel learning,MKL)在视觉语义概念检测中有广泛应用,但传统多核学习大都采用线性平稳的核组合方式而无法准确刻画复杂的数据分布.本文将精确欧氏空间位置敏感哈希(Exact Euclidean locality sensitivehashing,E2LSH)算法用于聚类,结合非线性多核组合方法的优势,提出一种非线性非平稳的多核组合方法-E2LSH-MKL.该方法利用Hadamard内积实现对不同核函数的非线性加权,充分利用了不同核函数之间交互得到的信息;同时利用基于E2LSH哈希原理的聚类算法,先将原始图像数据集哈希聚类为若干图像子集,再根据不同核函数对各图像子集的相对贡献大小赋予各自不同的核权重,从而实现多核的非平稳加权以提高学习器性能;最后,把E2LSH-MKL应用于视觉语义概念检测.在Caltech-256和TRECVID2005数据集上的实验结果表明,新方法性能优于现有的几种多核学习方法.

基于多核学习的单分类多示例学习算法

将多核学习引入到单分类多示例学习中,提出了一种基于多核学习的单分类多示例支持向量数据描述算法,解决了多核学习方法在实际应用中多示例数据具有比较复杂分布结构的学习问题。本文算法是将多个示例数据通过多个不同的核函数多核映射到特征空间,在特征空间中通过支持向量数据描述算法构建球形分类器。该算法采用迭代优化框架,首先,根据初始化包中的正示例来优化目标函数以此建立分类器。然后,根据上一步得到的分类器再对包中的正示例的标签进行更新。最后,在Corel、VOC 2007和Messidor数据集上的实验结果表明,所提出的算法比单核多示例方法具有更好的性能,进一步验证了算法的可行性和有效性。

基于KPCA和MKL-SVM的非线性过程监控与故障诊断

利用核主元分析非线性过程监控的优势,结合多重核学习支持向量机在故障诊断方面的准确性,提出了基于核主元分析和多重核学习支持向量机的非线性过程监控与故障诊断方法。该方法运用核主元法对数据进行处理,在特征空间构建T2和SPE来检测故障的发生,若有故障发生,则计算样本的非线性主元得分向量,将其作为MKL-SVM的输入值,通过MKL-SVM的分类进行故障类型识别。将上述方法应用到Tennessee Eastman(TE)化工过程,多种故障模式的仿真结果表明该方法不但能有效地辨识故障,而且提高了故障检测和故障诊断的速度。

基于GMKL-SVM的模拟电路故障诊断方法

提出了一种新颖的基于广义多核支持向量机(GMKL-SVM)的模拟电路故障诊断方法。首先,应用Haar小波分析提取被测电路时域响应信号的小波系数作为特征参量,并生成样本数据;然后,基于样本数据,应用量子粒子群算法对GMKL-SVM的参数进行优化,并以此建立基于GMKL-SVM的故障诊断模型,用于区分模拟电路的各个故障。实例电路的单故障和双故障诊断实验结果表明,所提出的GMKL-SVM方法能较好地实现模拟电路故障诊断,与传统的GMKL-SVM方法相比,表现出了更好的性能,获得了更高的故障诊断正确率。

基于递归定量分析与多核学习支持向量机的玻璃纤维增强复合材料缺陷识别技术

为了提高玻璃纤维增强复合材料(Glass Fiber Reinforced Polymer,GFRP)超声检测中缺陷识别技术的准确性,提出基于递归定量分析(Recurrence Quantitative Analysis,RQA)与多核学习支持向量机(MKLSVM)相结合的检测模型,以提高检测GFRP中不同类型缺陷的能力。结果表明,该模型能够准确识别GFRP中的分层缺陷与夹杂缺陷,检测识别率达到92.92%,并且与基于离散小波变换(Discrete Wavelet Transform,DWT)和经验模态分解(Empirical Mode Decomposition,EMD)的MKLSVM检测模型的识别率相比,所提出的检测模型的识别率分别提高了7.5%和3.75%。

链式回转弹仓区间不确定性动力学模型

针对具有区间不确定性参数的辨识问题,提出一种基于区间可能度转换模型的区间不确定性参数的双层嵌套辨识(Double-layer Nested Identification,DNI)方法。通过将待辨识参数分为两类,利用DNI方法辨识出第1类确定性参数,再通过基于DNI思想的区间优化方法优化第2类区间不确定性参数的区间范围;面向嵌套策略类型方法计算量庞大且效率低的问题,选用贝叶斯优化-粒子群优化(Bayesian Optimization-Particle Swarm Optimization,BO-PSO)方法作为内层算法以提高求解效率。DNI方法的内层利用BO-PSO方法计算区间上下界,外层利用改进型布谷鸟搜索(Improved Cuckoo Search,ICS)方法辨识特定参数。为进一步缩短求解时间,提出一种ICS多核极限学习机(ICS-Multiple Kernel-Extreme Learning Machine,ICS-MK-ELM)代理模型,ICS-MK-ELM代理模型克服了人工调节每个核函数超参数的困难,并且模型预测精度明显高于核ELM(Kernel ELM,KELM)和MK-ELM;将DNI方法应用于链式回转弹仓的参数辨识,解决了链式弹仓具有区间不确定性参数的辨识困难的问题,参数辨识结果表明所提DNI方法以及基于DNI思想的区间优化方法具有更高的精度和稳定性。

基于MPA优化MKL-FSVDD模型的聚合釜设备故障诊断

针对化工流程工业数据具有强非线性、易受噪声影响和故障为多分类的问题,提出一种基于海洋捕食者算法(MPA)优化多核学习-模糊支持向量机数据描述(MKL-FSVDD)的故障诊断方法。利用MKL构建的多核函数,弥补单核函数的局限性,对非线性故障数据分类具有较强的适应性;引入MPA对MKL-FSVDD模型的核参数进行高效寻优,解决核参数选择难题。通过在TE数据平台上的对照实验,验证MPA-MKL-FSVDD模型故障诊断的有效性能;最后将故障诊断模型应用于聚氯乙烯(PVC)聚合反应中,利用70m^(3)的聚合釜设备历史数据集进行仿真验证。结果表明该方法充分利用复杂样本集的数据信息,并在参数寻优阶段快速、稳定获得最优解,保证了故障分类的效率和准确度。

基于DCQGA-SMKL-SVM的模拟电路故障诊断方法

提出了双链量子遗传算法(DCQGA)优化简单多核支持向量机(SMKL-SVM)的模拟电路故障诊断方法。首先,提取测试电路时域响应信号,用Harr小波对响应信号进行变换并归一化处理,得到特征参数;其次,用双链量子遗传算法优化SMKL-SVM的参数,以此建立起DCQGA-SMKL-SVM故障诊断模型,用于模拟电路故障诊断。双二次滤波器电路与四运放二阶高通滤波器电路作为仿真测试电路,仿真测试结果表明,提出的故障诊断方法实现了模拟电路故障诊断,相比于DCQGA-SVM模拟电路故障诊断方法,诊断正确率更高。