A novel SVM+NDA model for classification with an application to face recognition

Support vector machine (SVM) is a powerful classification methodology, where the support vectors fully describe the decision surface by incorporating local information. On the other hand, nonparametric discriminant analysis (NDA) is an improvement over LDA where the normality assumption is relaxed. NDA also detects the dominant normal directions to the decision plane. This paper introduces a novel SVM+NDA model which can be viewed as an extension to the SVM by incorporating some partially global information, especially, discriminatory information in the normal direction to the decision boundary. This can also be considered as an extension to the NDA where the support vectors improve the choice of k-nearest neighbors on the decision boundary by incorporating local information. Being an extension to both SVM and NDA, it can deal with heteroscedastic and non-normal data. It also avoids the small sample size problem. Moreover, it can be reduced to the classical SVM model, so that existing softwares can be used. A kernel extension of the model, called KSVM+KNDA is also proposed to deal with nonlinear problems. We have carried an extensive comparison of the SVM+NDA to the LDA, SVM, heteroscedastic LDA (HLDA), NDA and the combined SVM and LDA on artificial, real and face recognition data sets. Results for KSVM+KNDA have also been presented. These comparisons demonstrate the advantages and superiority of our proposed model.     

Online nonparametric discriminant analysis for incremental subspace learning and recognition

This paper presents a novel approach for online subspace learning based on an incremental version of the nonparametric discriminant analysis (NDA). For many real-world applications (like the study of visual processes, for instance) it is impossible to know beforehand the number of total classes or the exact number of instances per class. This motivated us to propose a new algorithm, in which new samples can be added asynchronously, at different time stamps, as soon as they become available. The proposed technique for NDA-eigenspace representation has been used in pattern recognition applications, where classification of data has been performed based on the nearest neighbor rule. Extensive experiments have been carried out both in terms of classification accuracy and execution time. On the one hand, the results show that the Incremental NDA converges towards the classical NDA at the end of the learning process and furthermore. On the other hand, Incremental NDA is suitable to update a large knowledge representation eigenspace in real-time. Finally, the use of our method on a real-world application is presented.     

The capture and dissemination of integrated 3D geospatial knowledge at the British Geological Survey using GSI3D software and methodology

The Geological Surveying and Investigation in 3 Dimensions (GSI3D) software tool and methodology has been developed over the last 15 years. Since 2001 this has been in cooperation with the British Geological Survey (BGS). To-date over a hundred BGS geologists have learned to use the software that is now routinely deployed in building systematic and commercial 3D geological models. The success of the GSI3D methodology and software is based on its intuitive design and the fact that it utilises exactly the same data and methods, albeit in digital forms, that geologists have been using for two centuries in order to make geological maps and cross-sections. The geologist constructs models based on a career of observation of geological phenomena, thereby incorporating tacit knowledge into the model. This knowledge capture is a key element to the GSI3D approach. In BGS GSI3D is part of a much wider set of systems and work processes that together make up the cyberinfrastructure of a modern geological survey. The GSI3D software is not yet designed to cope with bedrock structures in which individual stratigraphic surfaces are repeated or inverted, but the software is currently being extended by BGS to encompass these more complex geological scenarios. A further challenge for BGS is to enable its 3D geological models to become part of the semantic Web using GML application schema like GeoSciML. The biggest benefits of widely available systematic geological models will be an enhanced public understanding of the sub-surface in 3D, and the teaching of geoscience students.     

批量工艺卡管理系统的补充开发

针对原批量工艺卡管理系统数据分散以及只管理文字数据不支持图形处理的问题,运用J2EE技术进行补充开发.采用B/S模式实现工艺卡远程操作;通过新建补充数据库和统一的工艺卡数据平台,实现数据统一管理和与原系统数据的平稳连接;采用SQL语句生成器减化了批量数据操作;采用XML技术完成文字加图形的工艺卡报表生成和打印.     

Developing a geoscience knowledge framework for a national geological survey organisation

Geological survey organisations (GSOs) are established by most nations to provide a geoscience knowledge base for effective decision-making on mitigating the impacts of natural hazards and global change, and on sustainable management of natural resources. The value of the knowledge base as a national asset is continually enhanced by the exchange of knowledge between GSOs as data and information providers and the stakeholder community as knowledge ‘users and exploiters’.Geological maps and associated narrative texts typically form the core of national geoscience knowledge bases, but have some inherent limitations as methods of capturing and articulating knowledge. Much knowledge about the three-dimensional (3D) spatial interpretation and its derivation and uncertainty, and the wider contextual value of the knowledge, remains intangible in the minds of the mapping geologist in implicit and tacit form.To realise the value of these knowledge assets, the British Geological Survey (BGS) has established a workflow-based cyber-infrastructure to enhance its knowledge management and exchange capability. Future geoscience surveys in the BGS will contribute to a national, 3D digital knowledge base on UK geology, with the associated implicit and tacit information captured as metadata, qualitative assessments of uncertainty, and documented workflows and best practice.Knowledge-based decision-making at all levels of society requires both the accessibility and reliability of knowledge to be enhanced in the grid-based world. Establishment of collaborative cyber-infrastructures and ontologies for geoscience knowledge management and exchange will ensure that GSOs, as knowledge-based organisations, can make their contribution to this wider goal.     

Feature space locality constraint for kernel based nonlinear discriminant analysis

Subspace learning is an important approach in pattern recognition. Nonlinear discriminant analysis (NDA), due to its capability of describing nonlinear manifold structure of samples, is considered to be more powerful to undertake classification tasks in image related problems. In kernel based NDA representation, there are three spaces involved, i.e., original data space, implicitly mapped high dimension feature space and the target low dimension subspace. Existing methods mainly focus on the information in original data space to find the most discriminant low dimension subspace. The implicit high dimension feature space plays a role that connects the original space and the target subspace to realize the nonlinear dimension reduction, but the sample geometric structure information in feature space is not involved. In this work, we try to utilize and explore this information. Specifically, the locality information of samples in feature space is modeled and integrated into the traditional kernel based NDA methods. In this way, both the sample distributions in original data space and the mapped high dimension feature space are modeled and more information is expected to be explored to improve the discriminative ability of the subspace. Two algorithms, named FSLC-KDA and FSLC-KSR, are presented. Extensive experiments on ORL, Extended-YaleB, PIE, Multi-PIE and FRGC databases validate the efficacy of the proposed method.     

A novel Bayesian logistic discriminant model: An application to face recognition

The linear discriminant analysis (LDA) is a linear classifier which has proven to be powerful and competitive compared to the main state-of-the-art classifiers. However, the LDA algorithm assumes the sample vectors of each class are generated from underlying multivariate normal distributions of common covariance matrix with different means (i.e., homoscedastic data). This assumption has restricted the use of LDA considerably. Over the years, authors have defined several extensions to the basic formulation of LDA. One such method is the heteroscedastic LDA (HLDA) which is proposed to address the heteroscedasticity problem. Another method is the nonparametric DA (NDA) where the normality assumption is relaxed. In this paper, we propose a novel Bayesian logistic discriminant (BLD) model which can address both normality and heteroscedasticity problems. The normality assumption is relaxed by approximating the underlying distribution of each class with a mixture of Gaussians. Hence, the proposed BLD provides more flexibility and better classification performances than the LDA, HLDA and NDA. A subclass and multinomial versions of the BLD are proposed. The posterior distribution of the BLD model is elegantly approximated by a tractable Gaussian form using variational transformation and Jensen's inequality, allowing a straightforward computation of the weights. An extensive comparison of the BLD to the LDA, support vector machine (SVM), HLDA, NDA and subclass discriminant analysis (SDA), performed on artificial and real data sets, has shown the advantages and superiority of our proposed method. In particular, the experiments on face recognition have clearly shown a significant improvement of the proposed BLD over the LDA.     

Artificial neural networks for feature extraction and multivariatedata projection

Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969) nonlinear projection, a linear discriminant analysis (LDA) network, a nonlinear discriminant analysis (NDA) network, and a network for nonlinear projection (NP-SOM) based on Kohonen's self-organizing map. A common attribute of these networks is that they all employ adaptive learning algorithms which makes them suitable in some environments where the distribution of patterns in feature space changes with respect to time. The availability of these networks also facilitates hardware implementation of well-known classical feature extraction and projection approaches. Moreover, the SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon's projection algorithm; the NDA method and NP-SOM network provide new powerful approaches for visualizing high dimensional data. We evaluate five representative neural networks for feature extraction and data projection based on a visual judgement of the two-dimensional projection maps and three quantitative criteria on eight data sets with various properties.     

生物基因测序高性能计算日志的任务分析及建模

生物基因测序是生物信息学分析中最常用的高性能计算任务.旨在通过分析生物基因测序日志找出生物基因测序日志中的任务特性,构建一种通用的适合分析生物基因测序的任务模型,并应用于面向基因测序的高性能计算系统的任务调度及性能优化.基于任务日志,主要分析了生物基因测序日志中任务到达时间的规律特性、任务运行时间和任务的并行尺寸等特性,通过这些任务特性利用指数分布、伽马分布、正态分布以及线性拟合构建了相应的局部任务模型,然后提出一种局部模型融合的方法,将各个局部模型合并为统一的任务模型.通过两种通用的模型评测方法对任务模型进行的评测结果显示,最终的任务模型与原有任务日志的4种任务属性趋于相同的分布,验证了所构建的任务模型具有很好的通用性.     

Robust large margin discriminant tangent analysis for face recognition

Fisher’s Linear Discriminant Analysis (LDA) has been recognized as a powerful technique for face recognition. However, it could be stranded in the non-Gaussian case. Nonparametric discriminant analysis (NDA) is a typical algorithm that extends LDA from Gaussian case to non-Gaussian case. However, NDA suffers from outliers and unbalance problems, which cause a biased estimation of the extra-class scatter information. To address these two problems, we propose a robust large margin discriminant tangent analysis method. A tangent subspace-based algorithm is first proposed to learn a subspace from a set of intra-class and extra-class samples which are distributed in a balanced way on the local manifold patch near each sample point, so that samples from the same class are clustered as close as possible and samples from different classes will be separated far away from the tangent center. Then each subspace is aligned to a global coordinate by tangent alignment. Finally, an outlier detection technique is further proposed to learn a more accurate decision boundary. Extensive experiments on challenging face recognition data set demonstrate the effectiveness and efficiency of the proposed method for face recognition. Compared to other nonparametric methods, the proposed one is more robust to outliers.     

A Fast Search Algorithm for a Large Fuzzy Database

In this paper, we propose a fast search algorithm for a large fuzzy database that stores iris codes or data with a similar binary structure. The fuzzy nature of iris codes and their high dimensionality render many modern search algorithms, mainly relying on sorting and hashing, inadequate. The algorithm that is used in all current public deployments of iris recognition is based on a brute force exhaustive search through a database of iris codes, looking for a match that is close enough. Our new technique, Beacon Guided Search (BGS), tackles this problem by dispersing a multitude of ldquobeaconsrdquo in the search space. Despite random bit errors, iris codes from the same eye are more likely to collide with the same beacons than those from different eyes. By counting the number of collisions, BGS shrinks the search range dramatically with a negligible loss of precision. We evaluate this technique using 632,500 iris codes enrolled in the United Arab Emirates (UAE) border control system, showing a substantial improvement in search speed with a negligible loss of accuracy. In addition, we demonstrate that the empirical results match theoretical predictions.     

Robust and efficient foreground analysis in complex surveillance videos

Mixture of Gaussians-based background subtraction (BGS) has been widely used for detecting moving objects in surveillance videos. It is very efficient and can update the background model with slow lighting changes, however, it suffers from a number of limitations in complex surveillance conditions such as quick lighting variations, heavy occlusion, foreground fragments, slow moving or stopped object etc. To address these issues, this paper first focuses on foreground analysis within the mixture of Gaussians BGS framework in long-term scene monitoring to handle (1) quick lighting changes, (2) static objects, (3) foreground fragments, (4) abandoned and removed objects, and (5) camera view changes. Then, we propose a framework with interactive mechanisms between BGS and processing from different high levels (i.e. region, frame, and tracking) to improve the accuracy of moving object detection and tracking to handle (1) objects that stop for a significant period of time and (2) slow-moving objects. The robustness and efficiency of the proposed mechanism are tested in IBM Smart Surveillance Solution on a variety of sequences, including standard datasets. The proposed method is very efficient and handles ten video streams in real-time on a 2GB Pentium IV machine with MMX optimization.     

网格原子化作业的二分图调度方法

对于网格系统中计算力调度等问题,结合有向无环作业图DATG和无向节点图UNG,采用并行集APS建立了一种基于二分图的网格调度算法BGS,并在惩罚策略、负载均衡、复活机制的引导下,使系统的调度动态地逐步趋向优化.实验结果表明:该算法能够更加适应网格资源的变化,降低作业负载,提高作业的并行化程度,并能根据系统负载合理地利用节点资源.     

基于精简星座鉴相的大频偏16-APSK信号载波同步

为了增大对16-APSK信号的鉴相范围,提高环路捕获带宽,提出了在环路捕获阶段使用精简星座鉴相、在捕获完成后使用混合NDA/DD鉴相的载波同步结构。这种结构在实现了环路捕获带宽增加的同时,仍然保持了混合NDA/DD法实现简单、与符号判决单元无耦合、运算速度快的优点。仿真结果验证了使用精简星座鉴相后环路捕获带宽的增加。     

多天线协同接收PSK载波参数估计的CRLB

针对利用多个独立天线对同一信号进行协同接收的问题,推导了数据辅助（DA）和非数据辅助（NDA）方式BPSK/QPSK信号频率及相位估计的Cramer-Rao下界（CRLB）,并与传统单个天线接收时的CRLB进行了比较。结果表明,在DA方式下,多天线协同接收PSK信号频率及相位估计的CRLB与传统单天线接收的CRLB相同;在NDA方式下,多天线协同接收的CRLB则要低于传统单天线接收的CRLB。     

Data and knowledge visualization with virtual reality spaces,neural networks and rough sets: Application to cancer and geophysical prospecting data

Visual data mining with virtual reality spaces is used for the representation of data and symbolic knowledge. High quality structure-preserving and maximally discriminative visual representations can be obtained using a combination of neural networks (SAMANN and NDA) and rough sets techniques, so that a proper subsequent analysis can be made. The approach is illustrated with two types of data: for gene expression cancer data, an improvement in classification performance with respect to the original spaces was obtained; for geophysical prospecting data for cave detection, a cavity was successfully predicted.     

Technical note: Replication graphs revisited

The article “Replication and consistency in a distributed environment” by Breitbart and Korth [Yuri Breitbart, Henry F. Korth, J. Comput. System Sci. 59 (1) (1999) 29-69] presents replication graphs as an efficient means to handle concurrency control in replicated databases.This technical note identifies and explains two inaccuracies in this article:

•

The basic global serializability-protocol BGS given by Breitbart and Korth [Yuri Breitbart, Henry F. Korth, Replication and consistency in a distributed environment, J. Comput. System Sci. 59 (1) (1999) 29-69] does not always guarantee serializability if combined with two-phase locking. We show that this problem can be avoided with a minor change to the protocol.

•

The theorem on minimal deadlock sets for the protocol BGS appears incorrect, and we give a counterexample to support this claim together with a brief discussion on the consequence of this.

Please note that this does not affect the applicability of replication graphs as a concept.     

A seamlessly coupled GIS and distributed groundwater flow model

There are three approaches for coupling groundwater models with GISs, i.e. loose, tight, and seamless. In seamless coupling a model code is written into, and run from within, a GIS. We implemented BGS GISGroundwater in a GIS in this way for the first time. It facilitates the construction and simulation of the model, and the visualisation of the results all within the GIS environment. The model consists of a 2D finite-difference groundwater flow model and a simple user-interface. It can represent heterogeneous aquifers, variably confined and unconfined conditions, and distributed groundwater recharge and abstraction. It offers benefits in terms of ease of use and in streamlining the model construction and application process. BGS GISGroundwater has been validated against analytical solutions to groundwater-head profiles for a range of aquifer configurations. This model lowers barriers to entry to groundwater flow modelling for a wider group of environmental scientists.