Human face recognition based on multidimensional PCA and extreme learning machine

In this work, a new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) is introduced. The proposed method is based on curvelet image decomposition of human faces and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique. Discriminative feature sets are generated using B2DPCA to ascertain classification accuracy. Other notable contributions of the proposed work include significant improvements in classification rate, up to hundred folds reduction in training time and minimal dependence on the number of prototypes. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques.     

基于流形学习的极端学习机及其在人脸识别中的应用

极端学习机以其快速高效和良好的泛化能力在模式识别领域得到了广泛应用,然而现有的ELM及其改进算法并没有充分考虑到数据维数对ELM分类性能和泛化能力的影响,当数据维数过高时包含的冗余属性及噪音点势必降低ELM的泛化能力,针对这一问题本文提出一种基于流形学习的极端学习机,该算法结合维数约减技术有效消除数据冗余属性及噪声对ELM分类性能的影响,为验证所提方法的有效性,实验使用普遍应用的图像数据,实验结果表明本文所提算法能够显著提高ELM的泛化性能。     

基于图像超分辨极限学习机的极低分辨率人脸识别

极低分辨率图像本身包含的判别信息少且容易受到噪声的干扰,在现有的人脸识别算法下识别率较低。为了解决这一问题,提出一种基于图像超分辨率(SR)极限学习机(ELM)的人脸识别算法。首先,从样本库学习耦合的高低分辨率图像稀疏表达字典,利用高低分辨率表达系数的流形一致性重建高分辨率图像;其次,在超分辨率重建的高分辨率(HR)图像上构建ELM模型,训练获得前向神经网络的连接权值;最后,通过ELM预测输入极低人脸图像的类别属性。实验结果表明,针对于重建后的极低分辨率人脸图片,与协同表示的分类(CRC)人脸识别算法相比,所提算法将识别率分别提升了2%;同时也大幅度缩短了识别的时间。结果表明所提算法能够有效解决极低分辨率图片判决信息不足的问题,具有较好的识别能力。     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

Dynamic ensemble extreme learning machine based on sample entropy

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.     

基于结构化遮挡编码和极限学习机的局部遮挡人脸识别

提出使用结构化遮挡编码（SOC）结合极限学习机（ELM）的算法来处理人脸识别中的遮挡问题。首先，使用SOC去除图像上的遮挡物，将遮挡物体与人脸分离开；同时，通过局部性约束字典（LCD）来估计遮挡物的位置，建立遮挡字典和人脸字典。然后，将建立好的人脸字典矩阵进行归一化处理，并利用ELM对归一化的数据进行分类识别。最后，在AR人脸库上进行的仿真实验结果表明，所提方法对不同遮挡物和不同区域遮挡的图像具有较好的识别率和鲁棒性。     

Human facial emotion recognition using improved black hole based extreme learning machine

Multimedia Tools and Applications - Facial Emotion Recognition (FER) plays an essential role in human-to-human communication and human-to-machine interaction. Based on the analysis of the facial...     

A survival ensemble of extreme learning machine

Due to the fast learning speed, simplicity of implementation and minimal human intervention, extreme learning machine has received considerable attentions recently, mostly from the machine learning community. Generally, extreme learning machine and its various variants focus on classification and regression problems. Its potential application in analyzing censored time-to-event data is yet to be verified. In this study, we present an extreme learning machine ensemble to model right-censored survival data by combining the Buckley-James transformation and the random forest framework. According to experimental and statistical analysis results, we show that the proposed model outperforms popular survival models such as random survival forest, Cox proportional hazard models on well-known low-dimensional and high-dimensional benchmark datasets in terms of both prediction accuracy and time efficiency.     

极限学习机集成在骨髓细胞分类中的应用

骨髓细胞的分类有重要的医学诊断意义。先对骨髓细胞图像分割和特征提取,用提取出来的训练集对极限学习机训练,再用该分类器对未知样本识别。针对单个分类器性能的不稳定,提出基于元胞自动机的极限学习机集成算法。通过元胞自动机抽样策略构建差异大的训练子集,多个分类器并行学习,多数投票法联合决策。实验结果表明,与BP、支持向量机比较,该算法基本无参数调整,学习速度快,分类精度高能达到97.33%,且有效克服了神经网络分类器不稳定的缺点。     

Robust and efficient face recognition via low-rank supported extreme learning machine

Multimedia Tools and Applications - Recently, face recognition algorithms have made great progress in various real-world applications, e.g., authentication and criminal investigation. Deep-learning...     

极限学习机的分类器集成模型研究

将极限学习机算法与旋转森林算法相结合,提出了以ELM算法为基分类器并以旋转森林算法为框架的RF-ELM集成学习模型。在8个数据集上进行了3组预测实验,根据实验结果讨论了ELM算法中隐含层神经元个数对预测结果的影响以及单个ELM模型预测结果不稳定的缺陷;将RF-ELM模型与单ELM模型和基于Bagging算法集成的ELM模型相比较,由稳定性和预测精度的两组对比实验的实验结果表明,对ELM的集成学习可以有效地提高ELM模型的性能,且RF-ELM模型较其他两个模型具有更好的稳定性和更高的准确率,验证了RF-ELM是一种有效的ELM集成学习模型。     

基于互信息的选择性集成核极端学习机

针对集成学习中的准确性和差异性平衡问题, 提出一种基于信息论的选择性集成核极端学习机. 采用具有结构简单、训练简便、泛化性能好的核极端学习作为基学习器. 引入相关性准则描述准确性, 冗余性准则描述差异性,将选择性集成问题转化为变量选择问题. 利用基于互信息的最大相关最小冗余准则对生成的核极端学习机进行选择, 从而实现准确性和差异性的平衡. 基于UCI 基准回归和分类数据的仿真结果验证了所提出算法的优越性.

     

基于自适应差分进化极限学习机的车牌识别算法

针对目前车牌识别算法中存在的模型训练慢、字符识别准确率低等问题,研究了一种基于自适应差分进化极限学习机的车牌识别算法。综合利用边缘检测和颜色定位的优点来检测车牌区域,然后用改进后的垂直投影法对车牌区域进行分割,最后将自适应差分进化极限学习机用于字符识别。研究结果表明,所提出算法具有训练速度快、字符识别率高等优点,可以应用于复杂的交通场景。     

基于改进极限学习机算法的行为识别

重点研究了极限学习机ELM对行为识别检测的效果。针对在线学习和行为分类上存在计算复杂性和时间消耗大的问题,提出了一种新的行为识别学习算法(ELM-Cholesky)。该算法首先引入了基于Cholesky分解求ELM的方法,接着依据在线学习期间核函数矩阵的更新特点,将分块矩阵Cholesky分解算法用于ELM的在线求解,使三角因子矩阵实现在线更新,从而得出一种新的ELM-Cholesky在线学习算法。新算法充分利用了历史训练数据,降低了计算的复杂性,提高了行为识别的准确率。最后,在基准数据库上采用该算法进行了大量实验,实验结果表明了这种在线学习算法的有效性。     

基于极限学习机的变压器故障快速识别

针对基于传统神经网络的变压器故障识别诊断方法存在网络收敛慢、易陷入局部极小点和网络参数难确定的缺点,提出了一种基于极限学习机的电力变压器故障快速识别方法。该方法以变压器油中用于故障类型分析的5种主要溶解气体含量作为输入特征量,5种常见变压器状态作为输出量建立分类识别模型。实验结果显示,该方法的识别准确率比支持向量机高12.5%,识别速度是支持向量机的2.6倍,比概率神经网络快5.5倍以上,表明该方法对变压器故障的识别快速而有效。     

基于多视图半监督集成学习的人体动作识别算法

移动设备上难以获取大量标签样本,而训练不足导致分类模型在人体动作识别上表现欠佳.针对这一问题,提出一种基于多视图半监督集成学习的人体动作识别算法.首先,利用两种内置传感器收集的数据构建两个特征视图,将两个视图和两种基分类器进行组合构建协同学习框架;然后,根据多分类任务重新定义置信度,结合主动学习思想在迭代过程中控制预测...     

集成优化核极限学习机的冠心病无创性诊断

冠心病的早期无创性诊断一直是医疗诊断领域的研究热点,为了提高冠心病诊断的准确率和诊断效率,提出了一种新颖的局部Fisher判别分析(LFDA)特征提取方法和集成核极限学习机(KELM)相结合的冠心病诊断模型(LFDA-EKELM)。首先使用LFDA方法剔除不相关特征和冗余特征,找出对分类结果贡献度较高的特征子集,产生不同的训练集以训练粒子群优化的KELM分类器PSO-KELM,并基于旋转森林(RF)构建集成分类器,实现冠心病的智能诊断。实验结果表明,与基于ELM、SVM和BPNN方法相比,提出方法有效提高了冠心病诊断准确率,提升了诊断效率,且分类结果高于已有方法和相似方法,是一种有效冠心病诊断模型。     

A new automatic target recognition system based on wavelet extreme learning machine

In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.     

基于在线序列极限学习机的车牌字符识别方法

当前基于神经网络方法的车牌字符识别技术都使用固定的训练样本,学习的效果受初始样本限制,对于识别过程中新出现的不同角度、光线等特征的字符图片不能自适应地学习。本文针对这个问题提出了基于在线序列极限学习机(OS-ELM)的车牌字符识别方法。在样本实时更新中使系统自适应调整网络权值,不断提高识别准确率。本文设计了汉字、字母、字母混合数字三个字符分类器,根据车牌字符的排列特征识别相应的字符。通过与传统ELM方法和BP神经网络法对比,结果证明该字符识别技术达到了较高的识别率,在训练速度上也比BP神经网络法提高了2~3个数量级。