Spectral clustering with eigenvector selection

The task of discovering natural groupings of input patterns, or clustering, is an important aspect of machine learning and pattern analysis. In this paper, we study the widely used spectral clustering algorithm which clusters data using eigenvectors of a similarity/affinity matrix derived from a data set. In particular, we aim to solve two critical issues in spectral clustering: (1) how to automatically determine the number of clusters, and (2) how to perform effective clustering given noisy and sparse data. An analysis of the characteristics of eigenspace is carried out which shows that (a) not every eigenvectors of a data affinity matrix is informative and relevant for clustering; (b) eigenvector selection is critical because using uninformative/irrelevant eigenvectors could lead to poor clustering results; and (c) the corresponding eigenvalues cannot be used for relevant eigenvector selection given a realistic data set. Motivated by the analysis, a novel spectral clustering algorithm is proposed which differs from previous approaches in that only informative/relevant eigenvectors are employed for determining the number of clusters and performing clustering. The key element of the proposed algorithm is a simple but effective relevance learning method which measures the relevance of an eigenvector according to how well it can separate the data set into different clusters. Our algorithm was evaluated using synthetic data sets as well as real-world data sets generated from two challenging visual learning problems. The results demonstrated that our algorithm is able to estimate the cluster number correctly and reveal natural grouping of the input data/patterns even given sparse and noisy data.     

A neural network-based scheme coupled with the RPV model inversion package

A hybrid scheme for the inversion of the Rahman-Pinty-Verstraete (RPV) model is presented. It combines the inversion technique described by Lavergne et al. (Lavergne, T., Kaminski, T., Pinty, B., Taberner, M., Gobron, N., Verstraete, M.M., Vossbeck, M., Widlowski, J.L., Giering, R. (2007). Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sensing of Environment, 107, 362-375.) and a multilayer backpropagation feedforward neural network. The RPV inversion package is applied to a sample set of pixels within the satellite scene. Subsequently the pairs of bidirectional reflectance factors (BRF) and model parameters estimated from the sample set of pixels are used to train the neural network. Since the mathematical formulation of the RPV model is embedded in these training data variables, the neural network can efficiently retrieve the model parameters for the whole satellite scene. This scheme has been tested for a MISR L2 BRF scene, MISR L1B2-derived BRF data corresponding to two different dates and a mosaic of MISR L2 BRF scenes acquired over Southern Africa covering a large extent of Miombo woodland. The results show this strategy retrieves the RPV model parameters and uncertainties with high accuracy and considerable speed over large areas.     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

服务接触中的不公平事件：从顾客的角度

This study was designed to answer several key questions regarding the service injustice customers perceived during the service delivery. Using the critical incident technique, the authors collected 195 unfair incidents from a variety of service organizations. The initial sorting of the incidents resulted in four major groups: information asymmetry, unfair procedure, inappropriate attitudes and manners of the service personnel, and unfair outcomes. Within the four major classifications of critical incidents, 13 secondary categories were identified. A further analysis was conducted to discover the underlying reason why customers feel unfair in the service encounters. Finally, some implications for service managers were discussed.     

在ASP.NET 2.0中自动化构建数据访问层

使用ASP.NET2.0所提供的BuildProvider类,将自动化构建的数据访问层源代码编译到程序集中,使得开发者利用Ob-jectDataSource控件,可以简单、快速地实现三层架构的Web应用。     

基于自适应过滤法和马尔柯夫链的网络流量预测方法

提出了一种新的网络流量预测方法,该方法根据网络流量历史值,用自适应过滤法进行预测,然后用马尔柯夫链对自适应过滤法预测时产生的误差进行修正,从而达到较高的预测精度。应用此模型对实际网络流量进行预测,结果表明了该方法是有效可行的。     

远程作业技术中公式编辑问题的解决方案

随着远程教育的不断发展,远程作业技术的研究与开发被提到越来越重要的位置。对于理工科专业的基础课来讲,学生在作业中需要输入大量的数学表达式,这些数学表达式最终必须由计算机自动进行识别和批改。针对这种情况,提出了适合于计算机自动识别的数据结构以及特殊结构的数据逻辑关系等,在此基础上对作业系统中的公式编辑器进行了设计和开发。     

基于FSVM光伏系统最大功率跟踪算法及仿真研究

针对光伏组件中常用的最大功率跟踪方法存在的不足,提出并建立了模糊支持向量机(FSVM)的最大功率点工作电压预测模型.通过对实测气象光强数据进行的仿真分析表明,与BP神经网络模型相比,FSVM的模型实现了结构风险最小化原则,对未来的样本有较好的泛化能力,具有更高的预测精度和稳定性.     

基于动态口令认证的VPN研究与设计

本文基于动态口令认证,构建一个部署代价不高,既能够安全认证,又能够安全传输的VPN系统。在当前最流行的I-Psec框架基础上,进行了一定的简化和修改,实现了一个IPsec体系的、具有基本VPN功能的原型系统。     

数据结构教学方法的探讨与研究

本文针对数据结构这门课程在教学中存在的问题．提出了一些解决方法。