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arXiv 2024年

作者： Li, Xuetong Gao, Yuan Chang, Hong Huang, Danyang Ma, Yingying Pan, Rui Qi, Haobo Wang, Feifei Wu, Shuyuan Xu, Ke Zhou, Jing Zhu, Xuening Zhu, Yingqiu Wang, Hansheng Guanghua School of Management Peking University Beijing China Center for Applied Statistics School of Statistics Renmin University of China Beijing China School of Economics and Management Beihang University Beijing China School of Statistics and Mathematics Central University of Finance and Economics Beijing China School of Statistics Beijing Normal University Beijing China School of Statistics and Management Shanghai University of Finance and Economics Shanghai China School of Statistics University of International Business and Economics Beijing China School of Data Science and MOE Laboratory for National Development and Intelligent Governance Fudan University Shanghai China

This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three categories of statistical computation methods: (1) distributed computing, (2) subsampling methods, and (3) minibatch gradient techniques. The first class of literature is about distributed computing and focuses on the situation, where the dataset size is too huge to be comfortably handled by one single computer. In this case, a distributed computation system with multiple computers has to be utilized. The second class of literature is about subsampling methods and concerns about the situation, where the sample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole. The last class of literature studies those minibatch gradient related optimization techniques, which have been extensively used for optimizing various deep learning models. © 2024, CC BY.

关键词： Distributed computer systems

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Special issue: Innovative applications of big data and artificial intelligence

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Frontiers of Engineering Management 2022年第4期9卷 517-519页

作者： W.Art CHAOVALITWONGSE Ye YUAN Qingpeng ZHANG Jianguo LIU Department of Industrial Engineering University of ArkansasFayettevilleAR 72701USA School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Data Science City University of Hong KongHong KongChina Institute of Accounting and Finance Shanghai University of Finance and EconomicsShanghai 200433China

Big data refers to complex data sets that cannot be processed using traditional data processing *** could also be defined as a large amount of unstructured or structured data from various *** intelligence(AI)refers to the science of expressing and acquiring knowledge using data,algorithms,and computing *** far,the big data industry chain is gradually mature,leading to a combination of big data and AI techniques developed in comprehensive *** cutting-edge technologies of big data and AI have played an increasingly important role in the innovative development of various industries such as epidemic prevention and control,emergency management,social governance,manufacturing,medical services,intelligent transportation,Internet of Things/supply chain,Internet industry,new media,and so *** addition,there are many typical innovative projects,which are being produced.

关键词： artificial services prevention

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A Study on The Multi-Attribute Decision Theory and Methods 9th

A Study on The Multi-Attribute Decision Theory and Methods

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Feng, Jinyuan Yan, Yunxi Huang, Manling Du, Yang Lu, Zhichen Li, Biao School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Dance Minzu University of China Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision theory

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Semiparametric Likelihood-based Inference for Censored data with Auxiliary Information from External Massive data Sources

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Acta Mathematicae Applicatae Sinica 2020年第3期36卷 642-656页

作者： Yue-xin FANG Yong ZHOU School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOEand Academy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062China

Published auxiliary information can be helpful in conducting statistical inference in a new *** this paper,we synthesize the auxiliary information with semiparametric likelihood-based inference for censoring data with the total sample size is *** express the auxiliary information as constraints on the regression coefficients and the covariate distribution,then use empirical likelihood method for general estimating equations to improve the efficiency of the interested parameters in the specified *** consistency and asymptotic normality of the resulting regression parameter estimators *** numerical simulation and application with different supposed conditions show that the proposed method yields a substantial gain in efficiency of the interested parameters.

关键词： Auxiliary information Massive data Censored data Empirical likelihood Estimation equations

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Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization

Efficient Document Retrieval by End-to-End Refining and Quan...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Qiu, Zexuan Su, Qinliang Yu, Jianxing Si, Shijing School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-Sen University Guangzhou China School of Economics and Finance Shanghai International Studies University China

Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances. However, existing semantic hashing methods are mostly established on outdated TFIDF features, which obviously do not contain lots of important semantic information about documents. Furthermore, the Hamming distance can only be equal to one of several integer values, significantly limiting its representational ability for document distances. To address these issues, in this paper, we propose to leverage BERT embeddings to perform efficient retrieval based on the product quantization technique, which will assign for every document a real-valued codeword from the codebook, instead of a binary code as in semantic hashing. Specifically, we first transform the original BERT embeddings via a learnable mapping and feed the transformed embedding into a probabilistic product quantization module to output the assigned codeword. The refining and quantizing modules can be optimized in an end-to-end manner by minimizing the probabilistic contrastive loss. A mutual information maximization based method is further proposed to improve the representativeness of codewords, so that documents can be quantized more accurately. Extensive experiments conducted on three benchmarks demonstrate that our proposed method significantly outperforms current state-of-the-art baselines. © 2022 Association for Computational Linguistics.

关键词： Embeddings

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Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models

arXiv

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arXiv 2022年

作者： Yu, Long Zhao, Peng Zhou, Wang School of Statistics and Management Shanghai University of Finance and Economics China School of Mathematics and Statistic Jiangsu Provincial Key Laboratory of Educational Big Data Science and Engineering Jiangsu Normal University Jiangsu Xuzhou China Department of Statistics and Data Science National University of Singapore Singapore

This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked eigenvalue is mainly determined by the order statistics of the bootstrap resampling weights, and follows extreme value distribution. Based on the disparate behavior of the spiked and non-spiked eigenvalues, we propose innovative methods to test the number of common factors. Indicated by extensive numerical and empirical studies, the proposed methods perform reliably and convincingly under the existence of both weak factors and cross-sectionally correlated errors. Our technical details contribute to random matrix theory on spiked covariance model with convexly decaying density and unbounded support, or with general elliptical *** Codes 62H25, 60B20 © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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On the Modelling and Prediction of High-Dimensional Functional Time Series

arXiv

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arXiv 2024年

作者： Chang, Jinyuan Fang, Qin Qiao, Xinghao Yao, Qiwei Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Sichuan Chengdu China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Business School University of Sydney Sydney Australia Faculty of Business and Economics The University of Hong Kong Hong Kong Department of Statistics London School of Economics London United Kingdom

We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. Our first step performs an eigenanalysis of a positive definite matrix, which leads to a one-to-one linear transformation for the original high-dimensional functional time series, and the transformed curve series can be segmented into several groups such that any two subseries from any two different groups are uncorrelated both contemporaneously and serially. Consequently in our second step those groups are handled separately without the information loss on the overall linear dynamic structure. The second step is devoted to establishing a finite-dimensional dynamical structure for all the transformed functional time series within each group. Furthermore the finite-dimensional structure is represented by that of a vector time series. Modelling and forecasting for the original high-dimensional functional time series are realized via those for the vector time series in all the groups. We investigate the theoretical properties of our proposed methods, and illustrate the finite-sample performance through both extensive simulation and two real datasets. © 2024, CC BY.

关键词： Time series

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Causally-Aware Unsupervised Feature Selection Learning

arXiv

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arXiv 2024年

作者： Shen, Zongxin Huang, Yanyong Wang, Dongjie Ma, Minbo Lv, Fengmao Li, Tianrui Joint Laboratory of Data Science and Business Intelligence School of Statistics Southwestern University of Finance and Economics Chengdu611130 China Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of irrelevant features and poor interpretability. Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph. To address these issues, a novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed. CAUSE-FS introduces a novel causal regularizer that reweights samples to balance the confounding distribution of each treatment feature. This regularizer is subsequently integrated into a generalized unsupervised spectral regression model to mitigate spurious associations between features and clustering labels, thus achieving causal feature selection. Furthermore, CAUSE-FS employs causality-guided hierarchical clustering to partition features with varying causal contributions into multiple granularities. By integrating similarity graphs learned adaptively at different granularities, CAUSE-FS increases the importance of causal features when constructing the fused similarity graph to capture the reliable local structure of data. Extensive experimental results demonstrate the superiority of CAUSE-FS over state-of-the-art methods, with its interpretability further validated through feature visualization. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Incomplete-view oriented kernel learning method with generalization error bound

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Information sciences 2021年 581卷 951-977页

作者： Tian, Yingjie Fu, Saiji Tang, Jingjing School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China

Multi-view learning (MVL) is a promising direction and most MVL methods work under the assumption that data are complete in all views. However, this assumption is often violated in practice due to the difficulties of high cost, equipment failure, and so on. Besides, the amount of data with missing views is particularly huge. Therefore, it is challenging yet valuable to fully leverage the large-scale incomplete-view data. Enlightened by the reduced support vector machine (RSVM) and multi-view privileged support vector machine (PSVM-2V), this paper proposes an efficient kernel method called reduced PSVM-2V (RPSVM-2V). It can not only provide a novel solution to process incomplete-view data, but also can be adjusted to address large-scale complete-view learning problem efficiently. In addition, the idea of replacing the full kernel with a smaller rectangular reduced kernel is extended to develop another two MVL methods, i.e., reduced SVM-2K (RSVM-2K) and reduced multiple kernel learning method (RMKL). Furthermore, we analyze the generalization error bounds of RPSVM-2V and two extensions by using Rademacher complexity. The comprehensive experiments demonstrate that our proposed models can achieve comparable performance with less time and memory cost. The spectral analysis has further verified the effectiveness of the reduced kernel used in our models. © 2021 Elsevier Inc.

关键词： Support vector machines

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Neighborhood Extension Enhanced Graph Focused Attention Networks for Social Recommendation

SSRN

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SSRN 2024年

作者： Li, Weiyue Zhu, Zhiguo Gao, Ming Zhang, Jun Liu, Zilong Fan, Weiguo School of Management Science and Engineering Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province Dongbei University of Finance and Economics China Department of Business Analytics Tippie College of Business University of Iowa United States

Social recommender systems are crucial in the context of social commerce platforms. Despite recent advancements in this field, we have identified two key limitations in existing research. Firstly, current social recommenders are hindered by the over-smoothing issue, making it challenging to effectively identify influential social friends within the higher-order neighborhood and provide corresponding explanations. Secondly, existing social recommenders overlook the multi-aspect hidden features of items and social relationships. To tackle the problems, the study introduces the Neighborhood Extension enhanced Graph focused Attention networks (short for NEGA). In contrast to soft attention, our NEGA model adopts focused attention to find the attractive products and the trusted social friends. To tackle the challenges posed by sparse social networks, the neighborhood extension method is proposed to further enhance recommendation performance. Additionally, we incorporate a parallel attention mechanism to capture the multi-aspect rating preferences and the multi-aspect impact of social relationships. Compared with twelve representative social recommenders on three real-world datasets, the baseline experiment demonstrates the competitive performance of our model. Finally, we conduct a series of visualized case studies to show the explainability of our model. The code is available at: https://***/usernameAI/NEGA. © 2024, The Authors. All rights reserved.

关键词： Social aspects

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