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Exploring big data Analysis: Fundamental Scientific Problems

Annals of data Science 2015年第4期2卷 363-372页

作者： Xu, Zongben Shi, Yong Department of Mathematics Xi’an Jiaotong University Xi’an China The Key Research Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

Although big data has been one of most popular topics since last several years, how to effectively conduct big data analysis is a big challenge for every field. This paper tries to address some fundamental scientific problems in big data analysis, such as opportunities, challenges, and difficulties encountered in the analysis. The challenges rise from multiple domains that include how management Science influences data acquisition and data management, Information Science for data access and processing, Mathematics and Statistics for data understanding and Engineering for data applications. The paper outlines six open research problems on big data. It also reports some advances on current big data research, particularly in high-dimensional data and non-structured data processing. Finally, remarks on how to develop a big data algorithm are provided. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： big data

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A Triple Structure of Rough Sets Based on Selection Function

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Annals of data Science 2015年第3期2卷 245-259页

作者： Wang, Bo Shi, Yong Tian, Yingjie Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

Different from rough sets in Pawlak’s sense, which is a binary approximation operations based structure, in this paper, we propose a new rough equivalence relation based on triple approximation operations induced by selection function. The same as traditional rough sets research, we consider the algebra issue of new rough sets system and construct lattice structure in an algebraic fashion. An isomorphic relationship is studied between proposed rough sets algebra structure and that in Pawlak’s sense. In addition, some examples are shown and studied in order to indicate the effectiveness of new equivalence relation in distinguishing and describing subsets of universe. Besides, we also study restriction of selection congruence on subalgebra built by covering of universe. Finally, conclusion on the axioms of middle approximation operation are shown to clarify properties of new structure. © 2015, Springer-Verlag Berlin Heidelberg.

关键词： Algebra

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A Survey on Feature Selection

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Procedia Computer Science 2016年 91卷 919-926页

作者： Jianyu Miao Lingfeng Niu School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100019 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset of the relevant features from the original features by removing irrelevant, redundant or noisy features. Feature selection usually can lead to better learning performance, i.e., higher learning accuracy, lower computational cost, and better model interpretability. Recently, researchers from computer vision, text mining and so on have proposed a variety of feature selection algorithms and in terms of theory and experiment, show the effectiveness of their works. This paper is aimed at reviewing the state of the art on these techniques. Furthermore, a thorough experiment is conducted to check if the use of feature selection can improve the performance of learning, considering some of the approaches mentioned in the literature. The experimental results show that unsupervised feature selection algorithms benefits machine learning tasks improving the performance of clustering.

关键词： feature selection machine learning unsupervised clustering

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Research on the Classification of Commercial Banks’ Fund Clients Based on Learning with Label Proportions

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Procedia Computer Science 2016年 91卷 988-994页

作者： Fuhai Ma Yong Shi Bo Wang Zhensong Chen Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China School of Management Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100190 China University of Nebraska Omaha Omaha NE 68106 USA

Due to the problem of privacy protection and other issues in reality, we cannot get the specific label of commercial banks’ fund client, which indicates whether each individual is an important client or not, but merely knows the proportion of certain type of clients. In this paper, a novel method, called learning with label proportions (LLP), is applied, by using Inverse Calibration algorithm to identify important commercial banks’ fund clients. The model shows promising results with high accuracy and good stability. The research is practical in solving privacy-preserving data analysis problems that cannot be processed with traditional method.

关键词： Learning with label proportions Inverse Calibration Important client identification

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Parameters Optimization for Nonparallel Support Vector Machine by Particle Swarm Optimization

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Procedia Computer Science 2016年 91卷 482-491页

作者： Seyed Mojtaba Hosseini Bamakan Huadong Wang Ahad Zare Ravasan Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research center on fictitious economy and data science Chinese Academy of Sciences Beijing 100190 China Department of Industrial Management Allameh Tabataba’i University Tehran Iran

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector Machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time.

关键词： Nonparallel Support Vector Machine Particle Swarm Optimization Twin SVM Parameter setting

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The Performance Analysis of Real Estate Listed Firms Based on the Sliding Time Window

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Procedia Computer Science 2016年 91卷 1038-1043页

作者： Cheng Qing Liu Xinyuan Sun Yi Guo Kun China University of Political Science and Law Beijing 102249 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Capital structure and operating capacity exert significant influence on firm's performance. According to the literature, the influence diverges from different business cycle. Based on a sample of real estate listed companies form 2004-2014, this paper dynamically investigates the different influence of capital structure and operating capacity on firm performance in a 3-year sling time window. The study found that there exists the Scale Economies Effect. After the economic crisis, the inventory turnover in days, representing the operation capacity, influence the performance greatly. While the capital structure influences the performance negatively though out the whole time period and the financing structure consistent with The Pecking Order Theory. This conclusion shows that the real estate listed firms are gradually returning to a more rational way.

关键词： real estate firm performance capital structure operation capacity sliding time window

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Determination and(Re)Parametrization of Rational Developable Surfaces

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Journal of Systems Science & Complexity 2015年第6期28卷 1426-1439页

作者： SHEN Liyong PREZ-DíAZ Sonia School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Dpto de Física y Matemáticas Universidad de Alcalá

The developable surface is an important surface in computer aided design, geometric modeling and industrial manufactory. It is often given in the standard parametric form, but it can also be in the implicit form which is commonly used in algebraic geometry. Not all algebraic developable surfaces have rational parametrizations. In this paper, the authors focus on the rational developable surfaces. For a given algebraic surface, the authors first determine whether it is developable by geometric inspection, and then give a rational proper parametrization in the affrmative case. For a rational parametric surface, the authors also determine the developability and give a proper reparametrization for the developable surface.

关键词： Parametrization proper rational developable surface reparametrization

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Multi-metrics Classification Machine

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Procedia Computer Science 2016年 91卷 556-565页

作者： Dewei Li Wei Zhang Dongkuan Xu Yingjie Tian School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Information Renmin University of China Beijing 100872 China Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Beijing 100190 China

Distance and its related decision rules are important in classification problems. k NN classifies a data point by the labels of its k -nearest neighbors and can be ameliorated by metric learning. For SVM, representative hyperplanes are found to refer to the location of every class and any point can be labeled by its perpendicular distance from the hyperplanes. Inspired by metric learning and SVM, a multi-metric classification machine, called MMCM, with a new prediction mechanism is proposed based on a novel distance relationship discerned by multi-metrics learning of the specificity information of each class. MMCM aims to find multi-metrics, namely the multiple local linear transformations for each class, to map data points into a new feature space, in which the distance between a point and its corresponding class centroid is minimized and data points of other classes are far from the centroid. An example with unknown label is classified according to the label of its nearest centroid. The primary problem is slacked as a linear optimization problem and kernel is introduced to make a nonlinear transformation. Enormous experiments verify MMCM's competitive performance both on binary classification and multi-class classification compared to state-of-the-art classification methods.

关键词： metric learning classification distance multi-metrics nonparallel SVM

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Image-regulated graph topic model for cross-media topic detection 15

Image-regulated graph topic model for cross-media topic dete...

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Wang, Zhiyi Li, Liang Zhang, Chunjie Huang, Qingming University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China

ISBN: (纸本)9781450335287

In recent years, pictures and videos have become ubiqui-tous on the Internet, which encourage the development of algorithm that analyze their semantic contents for detect-ing topics. Among them, topic modeling plays an essential role in discovering topics from document collections. Howev-er, with rich auxiliary information (such as geo-information, user-annotated tags, pictures and videos) rising up around the text, traditional topic models show their limitations to discover latent topics effectively from the cross-media data. To address this problem, we propose a novel Image-regulated Graph Topic Model (IGTM), which combines cross-media data together in the modeling process. By utilizing auxil-iary relation information among images, IGTM can achieve higher quality underlying topics as image relationships could serve as weakly-supervised information for topic modeling. Experimental results over two cross-media datasets demon-strate the effectiveness of our model. © 2015 ACM.

关键词： Semantics

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A direct fingerprint minutiae extraction approach based on convolutional neural networks

A direct fingerprint minutiae extraction approach based on c...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lu Jiang Tong Zhao Chaochao Bai A Yong Min Wu School of Computer & Control University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing China Beijing Eastern Golden Finger Technology Co. Ltd Beijing China

Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.

关键词： Neural networks Feature extraction Training data Training Convolution Electronic mail Computer architecture

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