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arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/Patternmining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

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Detecting Difference Between Coefficients in Linear Model Using Jackknife Empirical Likelihood

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Journal of Systems Science & Complexity 2016年第2期29卷 542-556页

作者： WU Xinqi ZHANG Qingzhao ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

Empirical likelihood has been found very useful in many different occasions. It usually runs into serious computational difficulties while jackknife empirical likelihood （JEL） is shown to be effective when applied to some complicated statistics. In this paper, to test the difference between coefficients of two linear regression models, the authors apply JEL to construct the confidence regions. Based on the 3EL ratio test, a version of Wilks＇ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. Simulation studies are carried out to show the effectiveness of the proposed method in aspects of coverage accuracy. A real data set is analyzed with the proposed method as an example.

关键词： Bartlett correction coverage accuracy Jackknife empirical likelihood linear regression model.

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Sample selection for noisy partial label learning with interactive contrastive learning

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Pattern Recognition 2025年 166卷

作者： Yu, Xiaotong Sun, Shiding Tian, Yingjie School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China China Aerospace Science & Industry Corp Defense Technology R&T Center Beijing100854 China 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 MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing100190 China China Unicom Research Institute Beijing100176 China

In the context of weakly supervised learning, partial label learning (PLL) addresses situations where each training instance is associated with a set of partial labels, with only one being accurate. However, in complex realworld tasks, the restrictive assumption may be invalid which means the ground-truth may be outside the candidate label set. In this work, we loose the constraints and address the noisy label problem for PLL. First, we introduce a selection strategy, which enables deep models to select clean samples via the loss values of flipped and original images. Besides, we progressively identify the true labels of the selected samples and ensemble two models to acquire the knowledge of unselected samples. To extract better feature representations, we introduce pseudo-labeled interactive contrastive learning to aggregate cross-network information of all samples. Experimental results verify that our approach surpasses baseline methods on noisy PLL task with different levels of label noise. © 2025 Elsevier Ltd

关键词： Contrastive Learning

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Empirical Likelihood Test for Regression Coefficients in High Dimensional Partially Linear Models

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Journal of Systems Science & Complexity 2021年第3期34卷 1135-1155页

作者： LIU Yan REN Mingyang ZHANG Sanguo School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

This paper considers tests for regression coefficients in high dimensional partially linear *** authors first use the B-spline method to estimate the unknown smooth function so that it could be linearly ***,the authors propose an empirical likelihood method to test regression *** authors derive the asymptotic chi-squared distribution with two degrees of freedom of the proposed test statistics under the null *** addition,the method is extended to test with nuisance *** show that the proposed method have a good performance in control of type-I error rate and *** proposed method is also employed to analyze a data of Skin Cutaneous Melanoma(SKCM).

关键词： Empirical likelihood test high dimensional analysis partially linear models regression coefficients

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Scale Adaptive Distractor Aware Visual Object Tracker

Scale Adaptive Distractor Aware Visual Object Tracker

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IEEE Conference on Recent Advances in Intelligent Computational Systems (RAICS)

作者： Kurup, Arundhati Key Laboratory of Big Data Mining and Knowledge Management School of Economics and Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538673362

Images and Videos are everywhere. They are being extensively used in numerous industrial, scientific and entertainment *** overabundance of data is propelled by the multimedia revolution. Image processing revolves around the ability of an algorithm to describe and manipulate images. However, video processing gives us the ability, to not only process individual frames of a video sequence but also to derive information from a collection of highly correlated frames over a time sequence. Visual Object Tracking is one of the most active research areas in Computer Vision. The objective of Visual Object Tracking is to monitor an object's spatial and temporal changes during a video sequence. It is used for analyzing sequential video frames and generating the movement of target between frames as output. In this paper, the problem of scale variance which is a challenge to visual object trackers is addressed. By making the object tracker scale invariant, a more precise scale adaptive bounding box can be drawn to predict the target location in a video sequence. Moreover this tracker adopts a Distractor-Aware tracking approach which considerably reduces the problem of drifting. This approach is highly suitable for time-critical and embedded-vision applications due to its low computational complexity and simplicity in *** proposed approach also allows for an efficient implementation of online tracking in real time.

关键词： Target tracking Visualization Image color analysis Adaptation models Video sequences Object tracking

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Exploring big data Analysis: Fundamental Scientific Problems

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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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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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Convergence of Stochastic Gradient Descent in Deep Neural Network

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Acta Mathematicae Applicatae Sinica 2021年第1期37卷 126-136页

作者： Bai-cun ZHOU Cong-ying HAN Tian-de GUO School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

Stochastic gradient descent(SGD) is one of the most common optimization algorithms used in pattern recognition and machine *** algorithm and its variants are the preferred algorithm while optimizing parameters of deep neural network for their advantages of low storage space requirement and fast computation *** studies on convergence of these algorithms were based on some traditional assumptions in optimization ***,the deep neural network has its unique *** assumptions are inappropriate in the actual optimization process of this kind of *** this paper,we modify the assumptions to make them more consistent with the actual optimization process of deep neural *** on new assumptions,we studied the convergence and convergence rate of SGD and its two common variant *** addition,we carried out numerical experiments with LeNet-5,a common network framework,on the data set MNIST to verify the rationality of our assumptions.

关键词： stochastic gradient descent deep neural network convergence

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Pyramid scheme model for consumption rebate frauds

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Chinese Physics B 2019年第7期28卷 549-556页

作者： Yong Shi Bo Li Wen Long 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 Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There are various types of pyramid schemes that have inflicted or are inflicting losses on many people in the *** propose a pyramid scheme model which has the principal characters of many pyramid schemes that have appeared in recent years: promising high returns, rewarding the participants for recruiting the next generation of participants, and the organizer takes all of the money away when they find that the money from the new participants is not enough to pay the previous participants interest and *** assume that the pyramid scheme is carried out in the tree network, Erd?s–Réney(ER) random network, Strogatz–Watts(SW) small-world network, or Barabasi–Albert(BA) scale-free *** then give the analytical results of the generations that the pyramid scheme can last in these *** also use our model to analyze a pyramid scheme in the real world and we find that the connections between participants in the pyramid scheme may constitute a SW small-world network.

关键词： pyramid scheme complex networks small-world networks

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