基于小波变换和AdaBoost极限学习机的癫痫脑电信号分类

针对单一极限学习机(ELM)在癫痫脑电信号研究中分类结果不稳定、泛化能力差的缺陷,提出一种基于互信息(MI)的AdaBoost极限学习机分类算法。该算法将AdaBoost引入到极限学习机中,并嵌入互信息输入变量选择,以强学习器最终的性能作为评价指标,实现对输入变量以及网络模型的优化。利用小波变换(WT)提取脑电信号特征,并结合提出的分类算法对UCI脑电数据集以及波恩大学癫痫脑电数据进行分类。实验结果表明,所提方法相比传统方法以及其他同类型研究,在分类精度和稳定性上有着明显提高,并具有较好的泛化性能。     

A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.     

基于多特征融合与极限学习机的植物叶片分类方法

对植物的分类多通过对植物叶片的分类来实现,为提高植物叶片分类的准确率提出了一种基于多特征融合与极限学习机的植物叶片分类方法.首先对植物叶片彩色图像进行预处理,得到去除叶片颜色与背景的二值图像和灰度图像;然后从二值图像中提取植物叶片的形状特征和不变矩特征,利用灰度图像提取灰度共生矩阵参数作为叶片图像的纹理特征,共得到28...     

基于Contourlet变换和支持向量机的纹理识别方法

针对变换域中图像纹理识别时如何选择最佳特征向量的问题,利用Contourlet变换的多方向、多尺度选择性和各向异性,将图像从空间域变换到频率域,全面地提取了Contourlet变换分解后低频子带、中频子带和高频子带的特征,输入支持向量机(SVM)分类器进行分类识别。利用Brodatz纹理库进行仿真实验,实验结果表明低频均值方差和高频能量作为组合特征时识别准确率可达98.75%,且特征向量维数少,是在Contourlet变换下表示图像纹理的最优特征。     

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

In this paper a novel multikernel deterministic extreme learning machine (ELM) and its variants are developed for classification of non-linear problems. Over a decade ELM is proved to be efficacious learning algorithms, but due to the non-deterministic and single kernel dependent feature mapping proprietary, it cannot be efficiently applied to real time classification problems that require invariant output solution. We address this problem by analytically calculation of input and hidden layer parameters for achieving the deterministic solution and exploiting the data fusion proficiency of multiple kernel learning. This investigation originates a novel deterministic ELM with single layer architecture in which kernel function is aggregation of linear combination of disparate base kernels. The weight of kernels depends upon perspicacity of problem and is empirically calculated. To further enhance the performance we utilize the capabilities of fuzzy set to find the pixel-wise coalition of face images with different classes. This handles the uncertainty involved in face recognition under varying environment condition. The pixel-wise membership value extracts the unseen information from images up to significant extent. The validity of the proposed approach is tested extensively on diverse set of face databases: databases with and without illumination variations and discrete types of kernels. The proposed algorithms achieve 100% recognition rate for Yale database, when seven and eight images per identity are considered for training. Also, the superior recognition rate is achieved for AT & T, Georgia Tech and AR databases, when compared with contemporary methods that prove the efficacy of proposed approaches in uncontrolled conditions significantly.

     

基于隐特征空间的极限学习机模型选择

针对极限学习机（ELM）中冗余的隐神经元会削弱模型泛化能力的缺点,提出了一种基于隐特征空间的ELM模型选择算法。首先,为了寻找合适的ELM隐层,在ELM中添加正则项,该项为现有隐层空间到低维隐特征空间的映射函数矩阵的Frobenius范数;其次,为解决该非凸问题,采用交替优化的策略,并通过凸二次型优化学习该隐空间;最终自适应得到最优映射函数和ELM模型。分别采用UCI标准数据集和载荷识别工程数据对所提算法进行测试,结果表明,与经典ELM相比,该算法可有效提高预测精度和数值稳定性,与现有模型选择算法相比,该算法预测精度相当,但运行时间则大幅降低。     

基于第二代Curvelet变换的点云曲面特征提取

针对点云特征提前取方法在多方向性分析方面的局限性,将Curvelet变换引入点云的分析,研究数据点云不同尺度曲面特征的提取方法。在数据点云分层、扩展预处理的基础上,以第二代离散Curvelet变换分析数据点云,采用软硬阈值折衷法,对表示数据点云边缘的Detail层、Fine层Curvelet变换系数进行处理,增强数据点云的边缘。对增强后的Curvelet变换系数进行Curvelet逆变换,重构数据点云,提取数据点云的边缘,获取曲面特征。实例表明,以Curvelet变换分析为基础的曲面特征提取方法,可以更加准确地提取数据点云的曲面特征。     

多尺度特征融合与极限学习机的玉米种子检测

目的 玉米种子等农作物检测是农业信息化领域中一个关键问题,为了能够快速和准确地实现对其检测,提出基于多尺度特征融合与极限学习机的玉米种子无损检测算法.方法 首先对种子特征的描述采用局部特征和全局特征相结合的特点,局部特征采用多尺度方向梯度直方图特征,而在全局特征上则提取HSV颜色模型特征.其次,针对传统的BP神经网络以及SVM等存在训练时间长、检测速度慢的不足,采用极限学习机作为其检测算法.此外,为了避免样本在训练时带来的过多时间消耗,该检测模型上采用了并行训练算法.再次,针对原始图像分辨率过高问题所带来的检测时间以及内存消耗较大的问题,采用一种基于局部均值的图像缩小算法.最后,针对该文采用的滑动窗口扫描可能带来的同一对象多窗口重叠的问题,提出了一种基于模糊聚类的局部窗口融合算法.结果 仿真结果表明,提出的方法可实现对玉米种子的准确检测,对检测样本的测试精度达到97.66%,同时误差控制在0.1%.结论 相比传统的方法,提出的方法在检测速度、检测准确率上均有所改善,无需严格的硬件设备要求并且对玉米种子检测时不会产生任何损伤.     

基于离散多小波变换的纹理分类

纹理分析在遥感、医学图像处理、计算机视觉及基于纹理的按内容检索的图像数据库等许多重要领域均有着广泛的应用.引入多小波理论,提出了基于多小波分解的纹理图像分类.通过一系列的实验并与单小波进行比较,实验结果表明,多小波分解比金字塔小波分解或小波包分解其分类准确率更优.     

基于影响度剪枝的极端学习机分类算法研究

针对极端学习机(ELM)网络规模控制问题,从剪枝思路出发,提出了一种基于影响度剪枝的ELM分类算法。利用ELM网络单个隐节点连接输入层和输出层的权值向量、该隐节点的输出、初始隐节点个数以及训练样本个数,定义单个隐节点相对于整个网络学习的影响度,根据影响度判断隐节点的重要性并将其排序,采用与ELM网络规模相匹配的剪枝步长删除冗余节点,最后更新隐含层与输入层和输出层连接的权值向量。通过对多个UCI机器学习数据集进行分类实验,并将提出的算法与EM-ELM、PELM和ELM算法相比较,结果表明,该算法具有较高的稳定性和测试精度,训练速度较快,并能有效地控制网络规模。     

Multiple-kernel-learning-based extreme learning machine for classification design

The extreme learning machine (ELM) is a new method for using single hidden layer feed-forward networks with a much simpler training method. While conventional kernel-based classifiers are based on a single kernel, in reality, it is often desirable to base classifiers on combinations of multiple kernels. In this paper, we propose the issue of multiple-kernel learning (MKL) for ELM by formulating it as a semi-infinite linear programming. We further extend this idea by integrating with techniques of MKL. The kernel function in this ELM formulation no longer needs to be fixed, but can be automatically learned as a combination of multiple kernels. Two formulations of multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of both toy and real-world data sets (including high-magnification sampling rate image data set) show that the resultant classifier is fast and accurate and can also be easily trained by simply changing linear program.     

Generalized eigenvalue extreme learning machine for classification

Applied Intelligence - Extreme learning machine (ELM) has attracted widespread attention due to its simple, quick and good performance. In this work, in order to deal with cross data quickly and...     

Brain tumor classification using modified kernel based softplus extreme learning machine

Multimedia Tools and Applications - An uncontrollable growth of abnormal cells in the brain may result in brain tumor. Two different categories of brain tumor are benign and malignant. The doctors...     

Probability based voting extreme learning machine for multiclass XML documents classification

This paper presents a novel solution based on Extreme Learning Machine (ELM) for multiclass XML documents classification. ELM is a generalized Single-hidden Layer Feedforward Network (SLFN) with extremely fast learning capacity. An improved vector model DSVM (Distribution based Structured Vector Model) is proposed to represent XML documents with more structural information and more precise semantic information. The XML documents classifiers are conducted based on PV-ELM (Probablity based Voting ELM) with a postprocessing method ε-RCC (ε - Revoting of Confusing Classes) to refine the voting results. To evaluate the overall performance of this solution, a series of experiments are conducted on two real datasets of news feeds online. The experimental results show that DSVM represents the XML documents more effectively and PV-ELM with ε-RCC achieves a higher accuracy than original ELM algorithm for multiclass classification.     

Representation learning with deep extreme learning machines for efficient image set classification

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

     

Land-use scene classification based on a CNN using a constrained extreme learning machine

ABSTRACT

Classifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.     

Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution

Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.

     

Fingerprint matching based on extreme learning machine

Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.     

Surface reconstruction based on extreme learning machine

In this paper, extreme learning machine (ELM) is used to reconstruct a surface with a high speed. It is shown that an improved ELM, called polyharmonic extreme learning machine (P-ELM), is proposed to reconstruct a smoother surface with a high accuracy and robust stability. The proposed P-ELM improves ELM in the sense of adding a polynomial in the single-hidden-layer feedforward networks to approximate the unknown function of the surface. The proposed P-ELM can not only retain the advantages of ELM with an extremely high learning speed and a good generalization performance but also reflect the intrinsic properties of the reconstructed surface. The detailed comparisons of the P-ELM, RBF algorithm, and ELM are carried out in the simulation to show the good performances and the effectiveness of the proposed algorithm.