基因表达数据的多尺度特征提取与分类研究

基于微阵列表达数据,探索新的有效特征提取和分类方法。采用小波多分辩率分析方法提取基因表达的特征,利用支持向量机和BP神经网络方法进行分类。基因表达具有明显的多尺度特征,分类率最大达到98.61％,结果稳定。采用多尺度理论对基因表达数据进行分析是一种新的有效的生物信息学方法,值得进一步探索与研究。     

分段提取函数型数据特征的算法研究

针对函数型数据分类算法中全局统计特征表达能力有限,且显著点特征易受噪声干扰等问题,提出一种基于统计深度方法的函数曲线特征分段提取算法。首先,利用数据平滑技术对离散观测的数据进行平滑化处理,同时引入函数型数据的一阶和二阶导函数;然后,分段计算函数本身及其低阶导函数的马氏积分深度值,在此基础上构造函数曲线特征向量;最后,给出三种选择调节参数的搜索方案,并进行分类研究。在UCR数据集上的实验表明,与当前其他曲线特征提取算法相比,所提算法能有效提取函数曲线特征,提高分类的准确性。     

Cluster analysis for gene expression data: a survey

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.     

基于张量分析的表情特征提取

表情识别的性能依赖于所提取表情特征的有效性,现有方法提取的表情基本上是人脸与表情的融合体,然而不同个体的人脸差异是表情识别的主要干扰因素。在表情识别时,理想情况是将个体相关的人脸特征和与个体无关的表情特征相分离。针对此问题,在三维空间建立人脸张量;然后用张量分析的方法将人脸特征与表情特征进行分离,使获取的表情参数与人脸无关。从而排除不同个体的人脸差异对表情识别的干扰。最后,在JAFFE表情数据库上验证了该方法的有效性。     

Ridge regression-based feature extraction for hyperspectral data

Feature extraction based on ridge regression (FERR) is proposed in this article. In FERR, a feature vector is defined in each spectral band using the mean of all classes in that dimension. Then, it is modelled using a linear combination of its farthest neighbours from among other defined feature vectors. The representation coefficients obtained by solving the ridge regression model compose the projection matrix for feature extraction. FERR can extract each desired number of features while the other methods such as linear discriminant analysis (LDA) and generalized discriminant analysis (GDA) have limitations in the number of extracted features. Experimental results on four popular real hyperspectral images show that the efficiency of FERR is superior to those of other supervised feature extraction methods in small sample-size situations. For example, for the Indian Pines dataset, the comparison between the highest average classification accuracies achieved by different feature extraction methods using a support vector machine (SVM) classifier and 16 training samples per class shows that FERR is 7% more accurate than nonparametric weighted feature extraction and is also 9% better than GDA. LDA, having the singularity problem in the small sample-size situations, has 40% less accuracy than FERR. The experiments show that generally the performance of FERR using the SVM classifier is better than when using the maximum likelihood classifier.     

一种加权矩形提取表情特征的方法*

表情特征提取是面部表情识别的一个关键步骤。针对目前特征提取效率低的情况,通过分析Gabor特征提取的性质和积分图像计算效率高的特点,提出一种利用积分图像技术和矩形模板计算面部特征点特征的方法,模板模拟Gabor的多尺度性,每个模板定义相应的权值,表情图像按照Gabor的各个方向旋转,使用旋转图像积分图和加权模板而不是在积分图上旋转模板提取特征点的特征值,最后将此特征值用于表情分类。实验结果表明,该方法在识别结果相当的情况下极大地提高了特征提取的效率。     

线特征提取的多尺度分析

线特征提取是计算机视觉中重要的低级处理过程,而多尺度分析是采用微分几何方法进行线特征提取时一个重要内容。研究了在对不同宽度线特征进行检测时,尺度因子的选择问题,分析了变化的线宽与特定尺度因子间的关系,得到新的尺度因子确定方法。实验表明该方法简单、省时、有效。     

Collaborative representation analysis methods for feature extraction

Recently, sparse representation (SR) theory gets much success in the fields of pattern recognition and machine learning. Many researchers use SR to design classification methods and dictionary learning via reconstruction residual. It was shown that collaborative representation (CR) is the key part in sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). Both SRC and CRC are good classification methods. Here, we give a collaborative representation analysis (CRA) method for feature extraction. Not like SRC-/CRC-based methods (e.g., SPP and CRP), CRA could directly extract the features like PCA and LDA. Further, a Kernel CRA (KCRA) is developed via kernel tricks. The experimental results on FERET and AR face databases show that CRA and KCRA are two effective feature extraction methods and could get good performance.

     

Supervised two-step feature extraction for structured representation of text data

Training data matrix used for classification of text documents to multiple categories is characterized by large number of dimensions while the number of manually classified training documents is relatively small. Thus the suitable dimensionality reduction techniques are required to be able to develop the classifier. The article describes two-step supervised feature extraction method that takes advantage of projections of terms into document and category spaces. We propose several enhancements that make the method more efficient and faster than it was presented in our former paper. We also introduce the adjustment score that enables to correct defected targets or helps to identify improper training examples that bias extracted features.     

An integrated approach for genome-wide gene expression analysis

Since efficient and relatively cheap methods were developed for determining biosequences, a lot of biosequence data has been generated. As the main problem in molecular biology is the analysis of the data instead of the data acquisition, part of the study of computational biology is to extract all kinds of meaningful information from the sequences. Computer-assisted methods have become very important in analyzing biosequence data. However, most of the current computer-assisted methods are limited to finding motifs. Genes can be regulated in many ways, including combinations of regulatory elements. This research is aimed at developing a new integrated system for genome-wide gene expression analysis. This research begins with a new motif-finding method, using a new objective function combining multiple well defined components and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. We then apply standard inductive learning algorithms to generate hypotheses for different gene behaviors. A genome-wide gene expression analysis demonstrated the value of this novel integrated system.     

基于三维特征提取的人体运动数据分析和检索

基于各关节点三维空间和时间关系的三维特征,提出了一种描述各关节点之间三维空间关系的时空特征,可以分别处理每一个关节点的局部时空特征。同时,三维时空特征避开与原始数据的直接接触,从而很大程度避免了维数灾难。实验结果显示,三维特征提高运动数据检索的效率和精度,可应用到运动语义智能分析等领域。     

Human vision inspired feature extraction for facial expression recognition

Multimedia Tools and Applications - Facial expression is a powerful way for human emotional communications. According to various applications, automatic facial expression recognition becomes an...     

无线传感器网络簇首提取压缩算法

对矢量数据压缩算法中DP压缩算法在引入无线传感器网络的同时进行了改进,针对压缩过程中对数据的扫描次数问题,提出簇首提取压缩算法。该算法中“簇首”即为“数据簇首”,簇首提取压缩算法设定步长减少压缩过程中对数据的扫描次数,并采用最佳曲线拟合方法对监测数据点做直线优化拟合,根据数据间的依附关系,将体现整体特征的簇首数据进行提取;同时,对非簇首数据进行子群划分。仿真结果表明,簇首提取压缩算法程序更为简单,对大波动数据有较好的簇首提取效果,减少了网络中数据的传输量,有效地节省了整个网络的能量消耗。     

基于可分度序的函数化数据分类特征提取方法

由于函数化数据的高维、高相关性特点,如何在保持其整体特性的前提下提取函数化数据的分类特征,是关系到能否有效提高分类效率和精度的关键问题。改进了当前常用的基于小波阀值法的函数化逐步降维方法,针对分类问题,借鉴信息论的思想,采用K-L可分度排序法构建了新的分类特征提取与降维规则。理论分析和实验表明,该方法能有效提取分类特征,提高分类效率、分类精度和分类稳健性。     

Adaptive linear discriminant analysis for online feature extraction

In this paper, we present new adaptive algorithms for the computation of the square root of the inverse covariance matrix. In contrast to the current similar methods, these new algorithms are obtained from an explicit cost function that is introduced for the first time. The new adaptive algorithms are used in a cascade form with a well-known adaptive principal component analysis to construct linear discriminant features. The adaptive nature and fast convergence rate of the new adaptive linear discriminant analysis algorithms make them appropriate for online pattern recognition applications. All adaptive algorithms discussed in this paper are trained simultaneously using a sequence of random data. Experimental results using the synthetic and real multiclass, multidimensional input data demonstrate the effectiveness of the new adaptive algorithms to extract the optimal features for the purpose of classification.     

Application of the wavelet transform for feature extraction in the analysis of hyperspectral laser-induced fluorescence data

Remote-sensing approaches for environmental protection and exploration have evolved rapidly in the last decade. Among the new operational tools, hyperspectral Fluorescent LiDAR System (FLS®) lidar has demonstrated a high sensitivity and the ability to function in complex environments for real-time, robust oil-spill monitoring on airborne or ship-borne analytical platforms. The capabilities of such analytical platforms include real-time analysis of laser-induced fluorescence (LIF) data. Although numerous examples of the application of signal theory to the analysis of hyperspectral data appear in the remote-sensing literature, the conventional data analysis strategies are not well adapted to the practical issues of the LIF applications. The aim of this article is to provide a new approach for LIF lidar analytical platforms, which is focused on the specifics of hyperspectral LIF data. The approach is based on structural data analysis and interpretation, through which more detailed spectral matching is performed. This article is based on a simulated experiment in which the spectra of actual seawater and well-known types of petroleum products were combined to demonstrate the wavelet-transform-based analysis of LIF data. The final part of the article demonstrates the application of the wavelet transform to the structural analysis of LIF data from field experiments for the detection and identification of oil products in difficult environmental conditions.     

Nonlinear feature extraction and classification of multivariate data in kernel feature space

Batch processes have played an essential role in the production of high value-added product of chemical, pharmaceutical, food, bio-chemical, and semi-conductor industries. For productivity and quality improvement, several multivariate statistical techniques such as principal component analysis (PCA) and Fisher discriminant analysis (FDA) have been developed to solve a fault diagnosis problem of batch processes. Fisher discriminant analysis, as a traditional statistical technique for feature extraction and classification, has been shown to be a good linear technique for fault diagnosis and outperform PCA based diagnosis methods. This paper proposes a more efficient nonlinear diagnosis method for batch processes using a kernel version of Fisher discriminant analysis (KFDA). A case study on two batch processes has been conducted. In addition, the diagnosis performance of the proposed method was compared with that of an existing diagnosis method based on linear FDA. The diagnosis results showed that the proposed KFDA based diagnosis method outperforms the linear FDA based method.