Manifold elastic net: a unified framework for sparse dimension reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al., Ann Stat 32(2):407–499, 2004), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: (1) the local geometry of samples is well preserved for low dimensional data representation, (2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, (3) the projection matrix of MEN improves the parsimony in computation, (4) the elastic net penalty reduces the over-fitting problem, and (5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.     

Community discovery using nonnegative matrix factorization

Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.     

Master‐slave synchronization for delayed Lur'e systems using time‐delay feedback control

This paper deals with the problem of designing delayed feedback controllers of master‐slave synchronization for Lur'e systems with time‐variant delay (0≤τ₀≤τ(t)≤τ_m). Through partitioning the intervals [0, τ₀] and [τ₀, τ_m], respectively, and choosing two augmented Lyapunov‐Krasovskii functionals(LKFs), two delay‐dependent synchronization criteria are formulated in the form of linear matrix inequalities (LMIs), in which the conservatism can be greatly reduced by thinning the partitioning of delay intervals and employing convex combination. Thus the sufficient conditions can be derived for the existence of delayed feedback controllers, and the controller gain matrices can be achieved by solving the established LMIs. Finally, three numerical examples are given to illustrate the presented synchronization schemes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

利用控制曲线和移动最小二乘法实现图像变形

将移动最小二乘方法用于图像变形,提出基于控制曲线的图像变形算法.根据源图像中的形状信息或变形需要来设置关键点,生成控制曲线,然后移动控制曲线到新的位置,利用移动最小二乘方法实现图像的变形.对基于控制曲线的移动最小二乘变形函数进行了理论推导,实现了图像的仿射、相似和刚性变换,得到不同的图像变形效果.实验表明,该算法可以较好地描述图像中的形状和轮廓信息,实现图像的复杂变形,获得真实感的变形效果.     

BeehiveZ:一个开放的业务过程模型管理框架

随着面向服务架构、业务过程管理技术的推广应用,企业中用建模语言描述的业务过程模型越来越多,这些模型描述了企业核心的业务逻辑,是企业最宝贵的知识资产.为了更好地促进企业的发展,提升竞争力,需要将这些业务过程模型作为数据进行有效地管理、建立企业过程模型库,并支持对过程模型的存储、浏览、相似性度量、检索等操作和应用开发.提出了一个开放的业务过程模型管理框架--BeeHiveZ,阐述了BeeHiveZ的体系结构、关键技术及系统功能与操作等内容.BeeHiveZ现已在开源软件社区SourceForge. net上发布.     

灰色理论在古陶瓷断代中的应用

利用模式识别方法可以推测古陶瓷出产年代。将古陶瓷中各化学成分的含量组成一个向量,并与该向量的灰色模型GM(1, 1)参数构成新向量,用于描述古陶瓷样品。以灰色关联度为距离定义,使用近邻法对古陶瓷样品年代进行预测,其留一法预测精度达79.55%。实验结果验证了灰色理论可以用于古陶瓷断代。     

基于自适应阈值设置的运动目标检测算法

提出一种基于自适应阈值设置的运动目标检测算法,该算法不同干传统的全局阈值设置方法,而是利用核密度对背景像素点进行密度估计,给出一种新的全局和局部阈值相结合的自适应阈值设置方法。该方法考虑不同位置的像素颜色分布复杂度不同,针对每个像素点自适应设置局部阈值,能克服全局阈值的不足,提高检测的精度。对多个标准视频进行实验,实验结果证明了该算法的有效性。     

基于内容相似度的网页正文提取

提出一种将复杂的网页脚本进行简化并映射成一棵易于操作的树型结构的方法。该方法不依赖于DOM树,无须用HTMLparser包进行解析,而是利用文本相似度计算方法,通过计算树节点中文本内容与各级标题的相似度判定小块文本信息的有用性,由此进行网页清洗与正文抽取,获得网页文本信息,实验结果表明,该方法对正文抽取具有较高的通用性与准确率。     

基于网络处理器的可演化路由器设计

可演化网络是一种动态可变换的网络结构,针对其软件实现方式导致的数据包转发延时较大问题,设计一种基于网络处理器的可演化路由器。充分利用网络处理器硬件中嵌入式处理器的灵活性与微引擎处理器快速高效的数据包处理能力,使数据包的处理速率接近 线速。     

具有QoS保障功能的服务网格资源映射策略

针对网格资源映射对用户的服务质量(QoS)偏好考虑不足的问题,对保障多维QoS的网格资源映射算法进行研究,从服务网格资源的多维QoS属性分析入手,在满足用户全局约束的前提下,根据服务中各子任务的QoS约束筛选出符合要求的网格资源,并给出合理的映射结果,提出一种网格资源映射算法——QoS-Sufferage算法。利用GridSim平台进行仿真比较,结果验证了算法的有效性。