一种自适应邻域选择半监督判别分析算法

为克服边界Fisher判别分析(MFA)只利用少量有标记样本和构建邻域不能充分反映流形学习对邻域要求的缺点,提出一种基于局部线性结构的自适应邻域选择半监督判别分析的算法。采用自适应算法扩大或者缩小近邻系数k来构建邻域以保持局部线性结构。MFA通过少量有类别标签样本进行降维的同时UDP对大量无标签样本进行学习,以半监督的方法对高维人脸数据进行维数约减。最后,在ORL和YALE人脸数据库通过实验结果验证了该算法的有效性。     

流形学习中基于局部线性结构的自适应邻域选择

近年来,流形学习成为包括机器学习、模式识别和计算机视觉等相关领域的研究热点.流形学习算法中,邻域选择直接关系到算法的性能,而传统的邻域选择算法如k近邻和ε邻域算法存在参数难以确定,所构建邻域不能反映流形学习算法对邻域要求等缺点.提出了一种基于流形局部线性结构的自适应邻域选择算法(ANSLL).首先通过分析现有流形学习算法,总结出构建邻域的两个基本原则:1)同一邻域的所有点都近似地位于某一d维线性子空间内(d为流形维数);2)每个邻域包含尽可能多的点.基于这两个基本原则,ANSLL 算法采用主成分分析技术(PCA)度量有限点集的线性程度,通过邻域压缩或扩张方式自适应地构建邻域.针对邻域线性结构的特点,还提出了一种改进的邻域图构建方法,以提高等度映射(Isomap)算法中测地线距离估计的准确性.最后大量系统的实验表明,ANSLL算法能够依据流形的局部曲率自适应地构建邻域,从而提高大多数流形学习算法(如Isomap和LLE)的性能.     

样本列信息与自适应邻域图的局部保持投影

针对局部保持投影(locality preserving projection,LPP)算法在传统k近邻构图过程中出现的参数k选择困难问题和样本的一维向量容易忽略样本的原始结构特征问题,引入样本的列信息思想,提出了一种基于样本对应列信息的自适应邻域构图的局部保持投影算法(adaptive neighbor and corresponding columns based graph construction on LPP,ANCCG-LPP)。该算法根据样本间的列信息自适应地得出所有样本列的列近邻,然后根据样本间成对的列近邻个数自适应地确定样本的邻域;最后通过重新定义权值矩阵来优化目标函数进行最优投影向量集的求解。在ANCCG-LPP算法的基础上,通过加入样本的类别信息,提出了有监督的ANCCGLPP算法。在ORL、Yale Extended B人脸库上的仿真实验验证了该算法的有效性。     

基于局部Fisher准则判别投影的人脸识别算法

为了提取更真实的样本局部分布结构以及合理利用样本标签信息,提出局部Fisher准则判别投影的人脸识别算法。通过求解样本在总体下稀疏表示来自适应选择样本的近邻参数,以使样本间分布关系尽可能符合真实情况;在获取稀疏近邻结构的基础上,利用样本标签信息设计自定义的类内局部散度矩阵和类间局部散度矩阵,以使得在保留样本间近邻关系的同时提高样本标签信息带来的判别能力。该算法可以有效保持同类样本间的稀疏近邻关系,并且破坏非同类样本间的稀疏近邻结构。在Yale库、AR库以及Yale B库上的实验结果表明:与相关的人脸识别算法相比,该算法具有更高的人脸识别率,可以有效提升人脸识别算法的识别率。     

基于近邻元分析的半监督流形学习算法*

现有的大多数流形学习算法偏重保持流形的几何结构,并未考虑到样本点的标签信息,这在一定程度上限制了流形学习算法在数据分类中的应用.因此文中提出一种基于近邻元分析的半监督流形学习算法,采用近邻元分析学习距离度量矩阵,在距离度量方式下选择样本点的局部邻域点.基于距离度量方式构造样本点和邻域点的局部几何结构,并在样本点的低维嵌入坐标中保持这种局部几何结构不变.3个不同数据集上的分类实验验证了文中算法的有效性.     

一种邻域自适应半监督局部Fisher判别分析算法

针对利用局部化思想解决多模数据的判别分析问题时,根据经验对局部邻域大小进行全局统一设定无法体现局部几何结构的差异性的不足,提出一种邻域自适应半监督局部Fisher判别分析（neighborhood adaptive semi-supervised local Fisher discriminant analysis,NA-SELF）算法。该算法在半监督局部Fisher判别分析算法的基础上,结合马氏距离和余弦相似度确定初始近邻数,并根据样本空间概率密度估计调整近邻数。通过人工数据集和5组UCI标准数据集对该算法的特征降维性能进行验证,并与典型的维数约简算法和采用传统k近邻方法的判别分析算法进行比较,实验结果表明该算法具备更高的有效性。     

基于监督协同近邻保持投影的人脸识别算法

基于流形学习理论的近邻保持嵌入算法(Neighborhood Preserving Embedding,NPE)能够发现数据集中隐含的内蕴结构,但当训练样本不足时,无法准确发现数据的内在流形结构,从而影响算法的识别效果.针对这一问题,对NPE算法进行改进,提出了监督协同近邻保持投影算法(Supervised Collaborative Neighborhood Preserving Projection,SCNPP).该算法在类别信息的指导下构建近邻图,使同类样本间的几何关系得到保持,利用协同表示弥补NPE因样本不足造成的表示误差,以一个有效保持样本近邻关系、准确发现数据内在流形结构的权值矩阵计算投影矩阵,提高分类效果.在FERET、AR和Extended Yale B人脸数据集上的实验验证了该算法的有效性.     

流形距离与压缩感知核稀疏投影的局部线性嵌入算法

流形学习算法的目的是发现嵌入在高维数据空间中的低维表示,现有的流形学习算法对邻域参数k和噪声比较敏感。针对此问题,文中提出一种流形距离与压缩感知核稀疏投影的局部线性嵌入算法,其核心思想是集成局部线性嵌入算法对高维流形结构数据的降维有效性与压缩感知核稀疏投影的强鉴别性,以实现高效有降噪流形学习。首先,在选择各样本点的近邻域时,采用流形距离代替欧氏距离度量数据间相似度的方法,创建能够正确反映流形内部结构的邻域图,解决以欧氏距离作为相似性度量时对邻域参数的敏感。其次,利用压缩感知核稀疏投影作为从高维观测空间到低维嵌入空间的映射,增强算法的鉴别性。最后,利用Matlab工具对实验数据集进行仿真,进一步验证所提算法的有效性。     

稀疏判别分析

针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,提出一种稀疏判别分析算法（SEDA）。首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用Fisher判别准则,能够得到最优的投影。在一组高维数据集上的实验结果表明,SEDA是非常有效的半监督降维方法。     

基于特征子空间邻域的局部保持流形学习算法*

局部保持流形学习算法通过保持局部邻域特性来挖掘隐藏在高维数据中的内在流形结构。然而,对于缺乏足够训练样本的高维数据集,或者高维数据集存在非线性结构和高维数据特征中存在冗余、干扰特征,使得在原特征空间中利用欧式距离定义的邻域关系并不能真实反映数据的内在流形结构,从而影响算法的性能。提出利用正约束寻找特征子空间的方法,使得在此子空间中更多的同类样本紧聚,并进一步在该子空间中构建邻域关系来挖掘高维数据的内在流形,形成基于特征子空间邻域特性的局部保持流形学习算法(NFS-LPP和NFS-NPE)。它们在一定程度上克服了高维小样本数据集难以正确挖掘内在流形结构的问题,在Yale和ORL人脸库上的分类和聚类实验验证了其有效性。     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.     

Editorial: Special issue on recent advances in hybrid control theory and applications

正The demands of a rapidly advancing technology for faster and more accurate controllers have always had a strong influence on the progress of automatic control theory.In recent years control problems have been arising with increasing frequency in widely different areas,which cannot be addressed using conventional control techniques.The principal reason for this is the fact that a highly competitive economy is forcing systems to operate in regimes where     

Information for Authors

正Aim The Journals of Zhejiang University-SCIENCE(A/B/C)areedited by the international board of distinguished Chinese andforeign scientists,and are aimed to present the latest devel-opments and achievements in scientific research in China andoverseas to the world’s scientific circles,especially to stimulateand promote academic exchange between Chinese and for-eign scientists everywhere.     

Enhancing a leaf radiative transfer model to estimate concentrations and in vivo specific absorption coefficients of total carotenoids and chlorophylls a and b from single-needle reflectance and transmittance

The relative concentrations of different pigments within a leaf have significant physiological and spectral consequences. Photosynthesis, light use efficiency, mass and energy exchange, and stress response are dependent on relationships among an ensemble of pigments. This ensemble also determines the visible characteristics of a leaf, which can be measured remotely and used to quantify leaf biochemistry and structure. But current remote sensing approaches are limited in their ability to resolve individual pigments. This paper focuses on the incorporation of three pigments—chlorophyll a, chlorophyll b, and total carotenoids—into the LIBERTY leaf radiative transfer model to better understand relationships between leaf biochemical, biophysical, and spectral properties.Pinus ponderosa and Pinus jeffreyi needles were collected from three sites in the California Sierra Nevada. Hemispheric single-leaf visible reflectance and transmittance and concentrations of chlorophylls a and b and total carotenoids of fresh needles were measured. These data were input to the enhanced LIBERTY model to estimate optical and biochemical properties of pine needles. The enhanced model successfully estimated reflectance (RMSE = 0.0255, BIAS = 0.00477, RMS%E = 16.7%), had variable success estimating transmittance (RMSE = 0.0442, BIAS = 0.0294, RMS%E = 181%), and generated very good estimates of carotenoid concentrations (RMSE = 2.48 µg/cm², BIAS = 0.143 µg/cm², RMS%E = 20.4%), good estimates of chlorophyll a concentrations (RMSE = 10.7 µg/cm², BIAS = − 0.992 µg/cm², RMS%E = 21.1%), and fair estimates of chlorophyll b concentrations (RMSE = 7.49 µg/cm², BIAS = − 2.12 µg/cm², RMS%E = 43.7%). Overall root mean squared errors of reflectance, transmittance, and pigment concentration estimates were lower for the three-pigment model than for the single-pigment model. The algorithm to estimate three in vivo specific absorption coefficients is robust, although estimated values are distorted by inconsistencies in model biophysics. The capacity to invert the model from single-leaf reflectance and transmittance was added to the model so it could be coupled with vegetation canopy models to estimate canopy biochemistry from remotely sensed data.     

Mr. Twinn’s bombes

This article discusses the history and design of the special versions of the bombe key-finding machines used by Britain’s Government Code & Cypher School (GC&CS) during World War II to attack the Enigma traffic of the Abwehr (the German military intelligence service). These special bombes were based on the design of their more numerous counterparts used against the traffic of the German armed services, but differed from them in important ways that highlight the adaptability of the British bombe design, and the power and flexibility of the diagonal board. Also discussed are the changes in the Abwehr indicating system that drove the development of these machines, the ingenious ways in which they were used, and some related developments involving the bombes used by the U.S. Navy’s cryptanalytic unit (OP-20-G).