基于KDDA和SFLA-LSSVR算法的WLAN室内定位算法

针对接收信号强度(received signal strength, RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis, KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过核函数策略将采集的各接入点(access point, AP)的RSS信号映射到非线性领域,有效提取了非线性定位特征,重组定位信息,去除冗余定位特征和噪声;然后采用LSSVR算法构建指纹点定位特征数据与物理位置的映射关系模型,采用SFLA算法优化该关系模型的参数,并用该关系模型对测试点的位置进行回归预测.实验结果表明:提出算法在相同的采样次数下的定位精度明显优于WKNN,ANN,LSSVR算法,并且在相同的定位精度下,采样次数较大减少,是一种性能良好的WLAN室内定位算法.     

基于模糊核LVQ的Sammon非线性映射算法

提出了一种基于可靠稳定的模糊核学习矢量量化（FKLVQ）聚类的Sammon非线性映射新算法。该方法通过Mercer核，将数据空间映射到高维特征空间，并在此特征空间上进行FKLVQ学习获取数据空间有效且稳定的聚类权矢量，然后在特征空间和输出空间上仅针对各空间的数据样本和它们各自的聚类权矢量进行Sammon非线性核映射。这样既降低了计算的复杂度，又使数据空间和输出空间上数据点与聚类中心间的距离信息保持相似。仿真结果验证了该方法的可靠性和稳定性。     

基于核方法的工频干扰消除算法

提出了一种新的基于核方法的工频干扰消除算法。利用构造参考信号的方法,将工频干扰消除问题转换为盲信号分离问题。通过核方法向高维特征空间的数据映射克服信号采集传感器的非线性畸变效应,利用信号的时间可预测性实现对输入信号的分离,从而得到不含工频干扰的有用信号。仿真实验结果表明,算法能够有效克服传感器的非线性畸变影响,很好地消除心电信号和地震信号中的工频干扰。     

基于改进支持向量机的Wi-Fi室内定位算法

针对室内复杂环境下无线信号不稳定、传统支持向量机定位算法计算复杂度高等难题,为了提高室内的定位精度,提出一种改进支持向量机的Wi-Fi室内定位算法。采用核主成分分析对特征进行降维处理,提取有用信息、降低计算量,采用支持向量机构建定位特征与物理位置的非线性映射模型,并采用粒子群算法对模型参数进行优化,进行了仿真实验。结果表明,该算法提高了室内定位精度和效率。     

基于半监督学习的室内定位算法

收集带有位置信息的经验样本即标定样本是一个花费昂贵的工作,限制了基于机器学习方法的实际应用。针对该问题,提出一种基于流形正则化的室内定位算法LocMR,该算法使用少量的标定样本和充足的未标定样本学习得出信号空间到位置空间的映射关系。在实际IEEE 802.11Wi-Fi环境中采集的数据集上进行验证,结果表明,LocMR在达到较高定位精确度的同时,能大幅减少定位系统的工作量,增强了其实际应用能力。     

基于核正交半监督鉴别分析的人脸识别算法

针对人脸识别中的非线性特征提取和有标记样本不足问题,提出了在核空间具有正交性半监督鉴别矢量的计算方法。算法利用核函数将人脸数据映射到高维非线性空间,在该空间采用边界Fisher判别分析（Marginal Fisher Analysis,MFA）算法将少量有类别标签样本进行降维,同时采用无监督鉴别投影（Unsupervised Discriminant Projection,UDP）对大量无标签样本进行学习,以半监督的方法构造算法的目标函数,在特征值求解时以正交方式找出最优投影向量,进行人脸识别。通过实验,在ORL和YALE人脸数据库上验证了该算法的有效性。     

核空间中智模糊聚类及图像分割应用

目的 为了更有效地提高中智模糊C-均值聚类对非凸不规则数据的聚类性能和噪声污染图像的分割效果,提出了核空间中智模糊均值聚类算法。方法 引入核函数概念。利用满足Mercer条件的非线性问题,用非线性变换把低维空间线性不可分的输入模式空间映射到一个先行可分的高维特征空间进行中智模糊聚类分割。结果 通过对大量图像添加不同的加性和乘性噪声进行分割测试获得的核空间中智模糊聚类算法提高了现有算法的对含噪声聚类的鲁棒性和分类性能。峰值信噪比至少提高0.8 dB。结论 本文算法具有显著的分割效果和良好的鲁棒性,并适应于医学,遥感图像处理需要。     

Semi-supervised kernel density estimation for video annotation

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in large-scale video annotation due to several difficulties, such as large variation of video content and intractable computational cost. In this paper, we propose a novel semi-supervised learning algorithm named semi-supervised kernel density estimation (SSKDE) which is developed based on kernel density estimation (KDE) approach. While only labeled data are utilized in classical KDE, in SSKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. It is a non-parametric method, and it thus naturally avoids the model assumption problem that exists in many parametric semi-supervised methods. Meanwhile, it can be implemented with an efficient iterative solution process. So, this method is appropriate for video annotation. Furthermore, motivated by existing adaptive KDE approach, we propose an improved algorithm named semi-supervised adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels rather than a fixed kernel, such that broader kernels can be applied in the regions with low density. In this way, more accurate density estimates can be obtained. Extensive experiments have demonstrated the effectiveness of the proposed methods.     

基于核最优变换与聚类中心的雷达目标识别

抽取有效鉴别特征是雷达一维高分辨距离像识别的关键.基干统计学习理论的核化原理,提出一种新的鉴别特征提取方法--核最优变换与聚类中心算法.该算法通过非线性变换,将数据映射到核空间,在核空间执行最优变换与聚类中心算法,能够提取一维距离像的稳健非线性鉴别特征.同时,基于训练样本在核空间所张成的子空间的一组基,给出一种快速计算方法,提高了特征提取速度.基于微波暗室实测数据的实验表明了该方法的有效性.     

Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem

An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis （PCA） show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.     

针对KASLR的Linux计时攻击方法

针对开启内核地址空间布局随机化（KASLR）防护的Linux系统,提出一种基于CPU预取指令的Cache计时攻击方法。Intel CPU的预取指令在预取未映射到物理地址的数据时会发生Cache失效,导致消耗的CPU时钟周期比已映射到物理地址的数据要长。根据这一特点,通过rdtscp指令获取CPU时钟周期消耗,利用计时攻击绕过KASLR技术防护,从而准确获取内核地址映射的Offset。实验结果表明,该攻击方法能够绕过Linux操作系统的KASLR防护,获得准确的内核地址映射位置,并且避免引起大量Cache失效。     

采用概率密度分布和粒子滤波的室内跟踪算法

提出了一种新的室内定位跟踪算法,采用了直方图法和核函数法估计参考点处的接收信号强度的概率分布,并将其作为该参考点的位置指纹,描述了该参考点处无线信道的特性;利用粒子滤波解决了非线性状态空间模型下的在线跟踪问题,仿真结果表明基于概率密度分布和粒子滤波的跟踪算法收敛速度快,且对环境变化不敏感,性能优于卡尔曼滤波算法。     

基于模糊核c-means 算法的位置指纹聚类

提出一种针对位置指纹的模糊核c-means聚类算法.将位置指纹归结为一种服从正态分布的区间值数据以反映接入点信号强度采样值的不确定性,通过区间中值和大小确定的正态分布函数将位置指纹映射为特征空间中的一点,并在该特征空间中采用基于核方法的模糊c-means算法对其进行聚类.通过ZigBee定位实验表明,该方法对于位置指纹的分类效果明显好于基于信号强度平均值的c-means聚类,可在保证定位精度的前提下有效降低定位的计算量.     

Cross-domain question classification in community question answering via kernel mapping

An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.     

极小化类内散布度的大间距非线性鉴别分析

提出了一种新的非线性鉴别分析算法——极小化类内散布的大间距非线性鉴别分析。该算法的主要思想是将原始样本映射到更高维的空间中,利用核技术对传统的大间距分类算法进行改进,在新的高维空间中利用再生核技术寻找核鉴别矢量,使得在这个新的空间中核类内散度尽可能的小。在ORL人脸数据库上进行实验,分析了识别率及识别时间,结果表明该方法具有一定优势。     

子空间半监督Fisher判别分析

Fisher判别分析寻找一个使样本数据类间散度与样本数据类内散度比值最大的子空间, 是一种很流行的监督式特征降维方法. 标注样本数据所属的类别通常需要大量的人工, 消耗大量的时间, 付出昂贵的成本. 为了解决同时利用有类别信息的样本数据和没有类别信息的样本数据用于寻找降维子空间的问题, 我们提出了一种子空间半监督Fisher判别分析方法. 子空间半监督Fisher判别分析寻找这样一个子空间, 这个子空间即保留了从有类别信息的样本数据中学习的类别判别结构, 也保留了从有类别信息的样本数据和没有类别信息的样本数据中学习的样本结构信息. 我们还推导了基于核的子空间半监督Fisher判别分析方法. 通过人脸识别实验验证了本文算法的有效性.     

一种改进的核特征抽取方法及其在人脸识别中的应用

首先利用核函数技术将原始样本隐式地映射到高维特征空间;然后在高维空间里利用再生核理论建立基于Fisher鉴别极小准则的2个等价模型;最后在该空间的核类间散布矩阵的非零空间和零空间中应用Fisher极小鉴别准则求取核鉴别矢量.在人脸库上的实验结果验证了该算法的有效性.     

基于核规范变量分析的非线性故障诊断方法

提出一种基于核规范变量分析（KCVA）的非线性过程故障诊断方法．该方法使用核函数完成非线性空间到高维线性空间的映射,避免了高维空间中的数据处理和非线性映射函数的使用．在线性空间中使用规范变量分析（CVA）来辨识状态空闻模型,从数据中提取状态信息．3个监测量（Tr^2,Ts^2,Q）用来进行故障检测,同时使用贡献图分离故障变量,并判断故障原因．在CSTR系统上的仿真结果表明,KCVA方法比主元分析法（PCA）和CVA方法能更灵敏地检测到故障的发生,更有效地监控过程变化．     

Design of semi-tensor product-based kernel function for SVM nonlinear classification

The kernel function method in support vector machine (SVM) is an excellent tool for nonlinear classification. How to design a kernel function is difficult for an SVM nonlinear classification problem, even for the polynomial kernel function. In this paper, we propose a new kind of polynomial kernel functions, called semi-tensor product kernel (STP-kernel), for an SVM nonlinear classification problem by semi-tensor product of matrix (STP) theory. We have shown the existence of the STP-kernel function and verified that it is just a polynomial kernel. In addition, we have shown the existence of the reproducing kernel Hilbert space (RKHS) associated with the STP-kernel function. Compared to the existing methods, it is much easier to construct the nonlinear feature mapping for an SVM nonlinear classification problem via an STP operator.