A new fuzzy approach for handling class labels in canonical correlation analysis

Canonical correlation analysis (CCA) can extract more discriminative features by utilizing class labels, especially the ones that can reflect the sample distribution appropriately. In this paper, a new fuzzy approach for handling class labels in the form of fuzzy membership degrees is proposed. We elaborately design a novel fuzzy membership function to represent the distribution of image samples. These fuzzy class labels promote the classification performances of CCA and kernel CCA (KCCA) through incorporating distribution information into the process of feature extraction. Comprehensive experimental results on face recognition demonstrate the effectiveness and feasibility of the proposed method.     

一种新的有监督的局部保持典型相关分析算法

从模式识别的角度出发,在局部保持典型相关分析的基础上,提出一种有监督的局部保持典型相关分析算法(SALPCCA)。该方法在构造样本近邻图时将样本的类别信息考虑在内,由样本间的距离度量确定权重,建立样本间的多重权重相关,通过使同类内的成对样本及其近邻间的权重相关性最大,从而能够在利用样本的类别信息的同时,也能保持数据的局部结构信息。此外,为了能够更好地提取样本的非线性信息,将特征集映射到核特征空间,又提出一种核化的SALPCCA(KSALPCCA)算法。在ORL、Yale、AR等人脸数据库上的实验结果表明,该方法较其他的传统典型相关分析方法有着更好的识别效果。     

Cultural dependency analysis for understanding speech emotion

Speech has been one of the major communication medium for years and will continue to do so until video communication becomes widely available and easily accessible. Although numerous technologies have been developed to improve the effectiveness of speech communication system, human interaction with machines and robots are still far from ideal. It is acknowledged that human can communicate effectively with each other through the telephony system. This situation motivates many researchers to study in depth the human communication system, with emphasis on its ability to express and infer emotion for effective social communication. Understanding the interlocutors’ emotion and recognizing the listeners’ perception is the key to boost communication effectiveness and interaction. Nonetheless, the perceived emotion is subjective and very much dependent on culture, environment and the pre-emotional state of the listener. Attempts have been made to understand the influence of culture in speech emotion and researchers have reported mixed findings that lead us to believe there are some common acoustical characteristics that enable similar emotion to be discriminated universally across culture. Yet there are unique speech attributes that facilitate exclusive emotion recognition of a particular culture. Understanding culture dependency is thus important to the performance of the speech emotion recognition system.In this paper three different speech emotion databases; namely: Berlin Emo-db, NTU_American and NTU_Asian dataset were selected to represent three different cultures of European, American and Asian respectively focusing on three basic emotions of anger, happiness and sadness with neutral acting as a reference. Different data arrangements with accordance to varying degree of culture dependency were designed for the experimental setup to provide better understanding of inter-cultural and intra-cultural effect in recognizing the speech emotion. Features were extracted using Mel Frequency Cepstral Co-efficient (MFCC) method and classified with neural network (Multi Layer Perceptron (MLP)) and fuzzy neural networks; namely: Adaptive Network Fuzzy Inference System (ANFIS) and Generic Self-Organizing Fuzzy Neural Network (GenSOFNN) representing precise and linguistic fuzzy rule conjuncts respectively. From the experimental results, it can be observed that culture influences the speech emotion recognition accuracy. 75% accuracy performance was recorded for generalized homogeneous intra-cultural experiments whereas the accuracy performance dropped to almost as low as chance probability (25% for 4 classes) for both homogeneous and heterogeneous mixed-cultural inter-culture experiments. The two-stage culture-sensitive speech emotion recognition approach was subsequently proposed to discriminate culture and speech emotion. Results of the analysis show potential of using the proposed technique to recognize culture-influenced speech emotion, which can be extended in many applications, for instance call center and intelligent vehicle. Such analysis may help us to better understand the culture dependency of speech emotion and as a result the accuracy performance of the speech emotion recognition system can be boosted.     

基于相容性分析的医疗诊断专家系统

医疗诊断专家系统作为人工智能重要应用领域之一,已被广泛应用于医疗诊断和网络医疗咨询等方面。其补充了人类专家的不足,并能有效地解决各种临床问题。然而,现有诊断推理模型在病例推理过程中过于依赖权值设定和医生经验值,诊断结果完全依赖于权值给定质量,并且目前尚未发现较实用的权值设定和自动赋值方法。为了解决权值设定和自动赋值的难题,提出了一种基于属性相容性分析的医疗诊断方法并进行了探讨。利用基于属性的相容性分析对医疗数据进行数字化处理,并根据属性之间的关联度构造属性相关矩阵和对属性进行剪枝处理,避免了无效属性的不必要运算;在相容性分析基础上提出了较为实用的权值设定数学模型;在进行病例相似度计算时,采用群体决策策略来完成诊断。针对所提方法进行了模拟。实验结果表明,该方法能有效解决权值自动设定问题和满足实际应用需求。     

A new index for molecular property correlation in halomethanes

“Geometric volume”, a new index, has been proposed for correlating boiling point, melting point, density and refractive index of 43 halomethanes. The index is a simple and straightforward approximation of the unit molecular volume in space. The index has been found to work well for other molecular properties and for other class of molecules as well.     

面向操作系统版本构建的软件包依赖关系分析

基于Linux的开源操作系统发行版本通常都是由一系列相互关联的软件包构建而成,由于软件包数量庞大、互相之间依赖和冲突等关系复杂,操作系统版本构建也变得复杂,定制裁剪的粒度和精度不足,可能存在很多冗余软件包。目前,操作系统版本的构建主要还是根据工程经验进行组织,特别是软件包的选择缺少理论分析和指导。给出基于依赖关系的操作系统版本构建的基本模型,并结合Ubuntu开源发行版本的版本构建过程和软件仓库数据,对软件包的出入度、优先级和分类等特征进行统计分析和模型验证,从依赖关系角度总结出操作系统版本构建的主要原则,为后续提升版本自动化定制裁剪的精度以及软件发行演化和故障分析提供指导和重要参考。     

一种新的用于连续值属性离散化的约简算法

针对在Nguyen和Skowron的离散化算法中进行启发式约简时会出现某些属性不能进行离散化问题，以及在无核数据集中启发式约简算法计算量比较大等问题，在粗糙集理论和属性频率函数的基础上给出一个新概念－候选核，并提出一种新的用于连续值属性离散化的约简算法－基于候选核的启发式约简算法（简称BCC）。该算法可以寻找到能对所有属性进行离散化的约简，实验表明，所提出的BCC算法能提高大数据集的离散化效果。     

A new definition of fuzzy functional dependency in fuzzy relational databases

The need to incorporate and treat information given in fuzzy terms in Relational Databases has concentrated a great effort in the last years. This article focuses on the treatment of functional dependencies (f.d.) between attributes of a relation scheme. We review other approaches to this problem and present some of its missfunctions concerning intuitive properties a fuzzy extension of f.d. should verify. Then we introduce a fuzzy extension of this concept to overcome the previous anomalous behaviors and study its properties. of primary interest is the completeness of our fuzzy version of Armstrong axioms in order to derive all the fuzzy functional dependencies logically implied by a set of f.f.d. just using these axioms. © 1994 John Wiley & Sons, Inc.     

A new logic correlation rule for HIV-1 protease mutation

Mining association rule in a large database is a technique for finding relations among attributes. In the last decade, most studies have been devoted to boost the efficiency, but few of them have been concentrated on the analysis of logic correlation among variables. Furthermore, mining association rules in a large database, when applied on a bio-sequence data set, is generally medically irrelevant and difficult to analyze. In this paper, a pre and post-processing approach through discovering a logic correlation rule by combining Apriori-based method and Boolean function simplification technique called Apriori_BFS method is presented. The objective of the proposed method is to effectively reduce the number of rules and present an integration logic correlation rule to readers. The experiment was conducted by using a real-world case, the HIV Drug Resistance Database, and its results unveil that the proposed method, Apriori_BFS, can not only present the logic correlation among variables but also provide more condensed rules than the Apriori method alone.     

A dependency annotation scheme for Bangla treebank

Dependency grammar is considered appropriate for many Indian languages. In this paper, we present a study of the dependency relations in Bangla language. We have categorized these relations in three different levels, namely intrachunk relations, interchunk relations and interclause relations. Each of these levels is further categorized and an annotation scheme has been developed. Both syntactic and semantic features have been taken into consideration for describing the relations. In our scheme, there are 63 such syntactico–semantic relations. We have verified the scheme by tagging a corpus of 4167 Bangla sentences to create a treebank (KGPBenTreebank).     

A statistical modeling methodology for the analysis of term structure of credit risk and its dependency

This paper presents a statistical modeling methodology for simultaneous estimation of the term structure for the risk-free interest rate, hazard rate, loss given default as well as credit risk dependency structure between bond-issuing industries. A model like this provides a realistic view for the market anticipation of credit risk for corporate bonds and the flexibility in capturing credit risk dependency between industries. Our statistical modeling procedure is carried out without specifying the model likelihood explicitly, and thus robust to the model mis-specification. An empirical analysis is conducted using the financial information on the Japanese bond market data. Numerical results confirm the practicality of the proposed methodology.     

基于核典型相关分析的多特征组合人脸识别

为了提高人脸的识别率,利用多特征和分类器之间的互补优势,提出一种基于核典型相关分析的多特征组合人脸识别方法（KCCA-MF）。提取人脸图像的LBP特征和Gabor特征,采用核典型相关分析算法对两种特征进行融合,以消除冗余特征,采用K近邻算法和支持向量机建立组合人脸分类器,并采用3个经典人脸库进行仿真分析。结果表明,相对于其他人脸识别方法,KCCA-MF提高了人脸识别的识别准确率和效率,可以满足人脸识别的实时性要求。     

典型相关分析融合全局和局部特征的人脸识别

为了提高人脸的识别率,提出一种典型相关分析融合全局和局部特征的人脸识别算法（SUB-CCA）。通过划分子模式方式避免人脸识别存在小样本、非线性问题,并提取局部特征,采用主成分分析提取人脸图像的全局特征,并采用相关分析算法对全局、局总特征进行融合,消除特征间冗余信息,降低特征维数,采用投票法得到人脸识别结果,并采用3个人脸数据集对算法性能进行测试。仿真结果表明,相对于参比算法,SUB-CCA提高了人脸识别的识别精度。     

A discriminant analysis using composite features for classification problems

In this paper, we propose a new discriminant analysis using composite features for pattern classification. A composite feature consists of a number of primitive features, each of which corresponds to an input variable. The covariance of composite features is obtained from the inner product of composite features and can be considered as a generalized form of the covariance of primitive features. It contains information on statistical dependency among multiple primitive features. A discriminant analysis (C-LDA) using the covariance of composite features is a generalization of the linear discriminant analysis (LDA). Unlike LDA, the number of extracted features can be larger than the number of classes in C-LDA, which is a desirable property especially for binary classification problems. Experimental results on several data sets indicate that C-LDA provides better classification results than other methods based on primitive features.     

A new measure for gene expression biclustering based on non-parametric correlation

Background

One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured.

Results

The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs.

Conclusions

SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance.     

Feature-driven requirement dependency analysis and high-level software design

In this paper, we introduce four kinds of dependency between features, namely refinement, constraint, influence and interaction, partly based on the observation of feature operationalization and assignment of responsibilities. Then, we explore three kinds of connection between these dependencies, that is, connections between refinement and constraint, between constraint and interaction, and between influence and interaction. We further show how to use the constraint dependencies to facilitate the customization-based reuse of requirements, and how to employ the result of feature dependency analysis to design high-level software architecture. A simple document editor and an email-client are used as running examples to illustrate the ideas introduced in this paper.