两阶段领域自适应学习

针对领域自适应问题中源域和目标域的联合分布差异最小化问题,提出两阶段领域自适应学习方法.在第一阶段考虑样本标签和数据结构的判别信息,通过学习一个共享投影变换,使投影后的共享空间中边缘分布的差异最小.第二阶段利用源域标记数据和目标域非标记数据学习一个带结构风险的自适应分类器,不仅能最小化源域和目标域条件分布差异,还能进一步保持源域和目标域边缘分布的流形一致性.在3个基准数据集上的实验表明,文中方法在平均分类准确率和Kappa系数两项评价指标上均表现较优.     

Robustness of classification rules that incorporate additional information

The discrimination problem for two normal populations with the same covariance matrix when additional information on the population is available is considered. A study of the robustness properties against training sample contamination of classification rules that incorporate this additional information is performed. These rules have received recently attention where their total misclassification probability (TMP) is proved to be lower than Fisher's linear discriminant rule. The results of a simulation study on the TMP which compares the behaviour of the new rules against Fisher's rule and some of its robustified versions under different types of contamination are presented. These results show that the rules that incorporate the additional information not only have lower TMP, but they also prevent against some types of contamination. In order to achieve prevention from all types of contamination a robustifed version of these rules is recommended.     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

基于线性判别分析的寄生虫卵识别

寄生虫病是危害人类及动物健康的疾病之一。为了实现对寄生虫卵的自动识别,辅助临床检测,提出基于线性判别分析的寄生虫卵识别方法。采用结合形态学滤波和Otsu的方法分割得到寄生虫卵及其轮廓,提取形状特征和纹理特征作为特征向量集,并利用线性判别分析实现对寄生虫卵自动识别。实验结果表明,该方法对6种寄生虫卵的识别正确率达到90.70%。     

边坡稳定性预测的Bayes判别分析方法及应用

边坡稳定性的分析是一个复杂的系统工程问题.基于Bayes判别分析(BDA)理论并结合工程实际,选用边坡岩体的重度黏聚力、摩擦角、边坡角、边坡高度及孔隙压力比等6个指标作为边坡稳定性预测的判别因子,建立边坡稳定性预测的Bayes判别分析模型;以32组边坡实测数据作为学习样本进行训练,建立Bayes线性判别函数;以交差确认估计法对判别准则进行评价以检验模型的优良性,以Bayes线性判别函数计算7个待判样品的Bayes判别函数值.研究表明:Bayes判别分类性能良好,与支持向量机方法有较好的一致性,且预测精度高,交差确认估计的误判率较低,为边坡稳定性预测提供了一种新思路.     

正则化半监督判别分析方法

为了克服加权线性判别分析(WLDA)只利用有标签的训练样本而不能反映样本数据流形结构的缺点,提出一种正则化的半监督判别分析方法。首先构建所有样本的近邻图来估计数据的局部流形结构,然后将此作为正则项引入WLDA的准则函数中。该方法避免了类内散度矩阵奇异,同时保持了样本数据的判别结构和几何结构。在ORL和YALE人脸数据库上的实验结果证明了该算法的有效性。     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

基于局部二值模式和级联AdaBoost的多模态人脸识别

提出了一种基于局部二值模式(LBP)和 级联AdaBoost的多模态人脸识别方法。采用级联AdaBoost算法分别从人脸深度图像和灰度图像的大量区域LBP直方图(RLBPH)中选出最有利于分类的少量特征,并连接成一个直方图向量,再分别用线性判别分析构建相应的线性子空间,用余弦相似度作为投影向量的相似度量,用求和规则进行信息融合。在FRGC数据库上的实验结果表明,提出的方法采用少量的特征取得了很好的识别效果,等错误率仅为1.40%。     

基于FLDA、CPCA与HMM的人脸识别

为了获得具有较高识别率的算法,提出了一种将Fisher线性鉴别分析(Fisher Linear Discriminant Analysis)、复主分量分析(Principal Analysis in the Complex Space)与隐马尔可夫模型(Hidden Markov Models)相结合进行人脸识别的方法。对于输入的不同光照、人脸表情和姿势的图像先进行归一化处理,然后将归一化后的图像转化成一维向量,再用FLDA方法提取每幅图像的特征,形成新的复向量空间;通过运用复主分量分析,来抽取人脸图像的有效鉴别特征;最后通过HMM对这些特征进行训练,得到一个优化的HMM并应用于识别。在ORL人脸数据库中进行实验,实验结果表明,该方法具有较高的识别率。