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arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Tencent Ai Lab Shenzhen518057 School of Mathematical Sciences Peking University Beijing100871 China DeepWise Ai Lab Beijing100085 Inst. of Info. Engin. Cas Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate (MLE) *** prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-ofthe-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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A generalized concept-cognitive learning: A machine learning viewpoint

arXiv

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arXiv 2018年

作者： Mi, Yunlong Shi, Yong Li, Jinhai School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha NE68182 United States Faculty of Science Kunming University of Science and Technology Kunming650500 China

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Metric learning for multi-instance classification with collapsed bags

Metric learning for multi-instance classification with colla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dewei Li Dongkuan Xu Jingjing Tang Yingjie Tian Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509061839

As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between bags in multi-instance problem, let alone learning appropriate metric for the problem. In this paper, we propose a new approach for multi-instance classification, with the idea of metric learning embedded. A new kind of distance is used to measure the similarity between bags. To weaken redundant information from bags and reduce computation complexity, k-means method is implemented to get collapsed bags by replacing each instance with its corresponding cluster centroid. The aim of metric learning is to expand inter-class bag distance and shrink intra-class bag distance, leading to the construction of an optimization problem with maximal relative distance. Kernel function can be introduced into the model to extract nonlinear information from the inputs. Gradient descent is utilized to solve the problem effectively. Numerical experiments on both artificial datasets and benchmark datasets demonstrated that the method can obtain competitive performance comparative to kNN and the state-of-the-art method in multi-instance classification.

关键词： Measurement Feature extraction data mining Optimization Kernel Training Drugs

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Metric based on multi-order spaces for cross-modal retrieval

Metric based on multi-order spaces for cross-modal retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Zhang Bingpeng Ma Guorong Li Qingming Huang Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Computer and Control Engineering University of Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China

ISBN: (纸本)9781509060689

This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.

关键词： Hilbert space Manifolds Correlation Covariance matrices Feature extraction Linear programming

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Risk Spillover Effect of Chinese Commercial Banks: Based on Indicator Method and CoVaR Approach

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Procedia Computer Science 2017年 122卷 932-940页

作者： Xiu-Qi Fan Meng-Di Du Wen Long Bussiness School of China University of Political Science and Law 102249 Beijing China CAS Research Center On Fictitious Economy & Data Science 100190 Beijing China School of Economics and Management UCAS 100190 Beijing China Key Laboratory of Big Data and Knowledge Management Chinese Academy of Sciences 100190 Beijing China

This paper took the thirteen listed commercial banks in China as the research objects, and used the financial risk measurement method CoVaR to study the risk spillover effect of commercial banks. Firstly, we employed an indicator-method to study the systemic importance changes of the listed banks in the normal and risk periods. It is found that the systemic importance is always closely related to the size of bank’s assets. The application of Cluster Analysis on the indicator score showed that they were divided into two part: four state-owned banks and nine joint-stock banks groups. From the perspective of financial market risk spillover effect caused by relevance, the risk contributions of each bank to the bank groups were calculated by using the quantile regression and CoVaR approaches in two time periods. The results showed that not only the systemically important banks with big asset scale had great risk spillover effect, in the risk period, some of the smaller scale banks had a significant increase in the risk contribution, which would also become an important source of risk. From the normal period to risk period, the risk spillover effects of each bank were obviously enhanced, and the risk contagion between banks rose generally. Therefore, regulators should not only focus on large-scale banks, but also pay attention to those banks with high risk contributions.

关键词： commercial banks risk spillovers CoVaR quantile regression

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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

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Procedia Computer Science 2017年 122卷 1162-1167页

作者： Luyao Zhu Wei Li Kun Guo Yong Shi Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy & Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

关键词： kernel function sentiment vector word embedding sentiment analysis

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Analysis of the Relation between Artificial Intelligence and the Internet from the Perspective of Brain Science

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Procedia Computer Science 2017年 122卷 377-383页

作者： Feng Liu Yong Shi Peijia Li Research Center on Fictitious Economy and Data Science the Chinese Academy of Sciences Beijing 100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

Artificial intelligence (AI) like deep learning, cloud AI computation has been advancing at a rapid pace since 2014. There is no doubt that the prosperity of AI is inseparable with the development of the Internet. However, there has been little attention to the link between AI and the internet. This paper explores them with brain insights mainly from four views:1) How is the general relation between artificial intelligence and Internet of Things, cloud computing, big data and Industrial Internet from the perspective of brain science. 2) Construction of a new AI system model with the Internet and brain science.

关键词： Internet’s virtual brain big SNS AI model of the Internet-like brain

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Large-scale Nonparallel Support Vector Ordinal Regression Solver

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Procedia Computer Science 2017年 108卷 1261-1270页

作者： Huadong Wang Jianyu Miao Seyed Mojtaba Hosseini Bamakan Lingfeng Niu Yong Shi Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68118 USA

Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is considerably fast during the process of training and testing. However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed model. Our experiments are performed on large document data sets to demonstrate the effectiveness of the proposed method.

关键词： Ordinal Regression Support Vector Machine ADMM

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Laplacian SVM for Learning from Label Proportions

Laplacian SVM for Learning from Label Proportions

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Limeng Cui Zhensong Chen Fan Meng Yong Shi School of Computer and Control Engineering UCAS School of Economics and Management UCAS Key Laboratory on Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9781509059119

Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term. Extensive experiments show the effectiveness of our method.

关键词： Support vector machines Laplace equations Kernel Training Optimization data models data mining

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Intelligence quotient and intelligence grade of artificial intelligence

arXiv

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arXiv 2017年

作者： Liu, Feng Shi, Yong Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Although artificial intelligence (AI) is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat,this study proposes a "standard intelligence model" that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture;testing and ranking the intelligence quotient (IQ) of naturally and artificially intelligent systems, including humans, Google, Microsoft's Bing, Baidu, and Siri;and finally, the dividing of artificially intelligent systems into seven grades from robots to Google Brain. Based on this, we conclude that Google's AlphaGo belongs to the third grade. Copyright © 2017, The Authors. All rights reserved.

关键词： Intelligent systems

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