Xuegang Hu

Hefei University of Technology

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Peipei Li

Hefei University of Technology

Xindong Wu

Hefei University of Technology

Yuhong Zhang

Hefei University of Technology

Gongqing Wu

Hunan University of Science and Technology

Lei Li

Xidian University

Chenyang Bu

Hefei University of Technology

Yi Zhu

Jiangsu Normal University

Fei Xie

Yunnan University

Kui Yu

Hefei University of Technology

Yaojin Lin

Zhangzhou Normal University

Cooperative Institutions

Hefei University of Technology

151

Chinese Academy of Sciences

Zhejiang University

University of Science and Technology of China

University of Chinese Academy of Sciences

Tsinghua University

Ministry of Education of the People's Republic of China

Beijing University of Posts and Telecommunications

Central South University

Shandong University

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A BIT-PARALLEL ALGORITHM FOR SEQUENTIAL PATTERN MATCHING WITH WILDCARDS

Cybernetics & Systems (2011)

Dan Guo Xiao-Li Hong Xuegang Hu Jun Gao Ying‐Ling Liu

Pattern matching with both gap constraints and the one-off condition is a challenging topic, especially in bioinformatics, information retrieval, and dictionary query. Among the algorithms to solve the problem, the most efficient one is SAIL, which is time consuming, especially when the pattern is long. In addition, existing algorithms based on bit-parallelism cannot handle a pattern that has only one pattern character between successive wildcards and the minimum local length constraints are zero. We propose an algorithm BPBM to handle online sequential pattern matching. In BPBM, an extended bit-parallelism operation is used to accelerate the matching process. An effective transition window mechanism with two nondeterministic finite state automatons (NFAs) is adopted to drop the useless scan window. It identifies gap constraints automatically and just scans once to export occurrences with exact match positions. Theoretical analysis and experimental results show that the BPBM algorithm is more competitive than other peers. It has an absolute advantage on search time complexity. It also has better stability that decreases operation costs with the increasing of the size of sequence alphabet or the length of the pattern. We also study off-line pattern matching. With twice pruning, left-most and right-most, we can increase the matching ratio about 2.08% on average.

Nondeterministic algorithm

Pruning

Sequence (biology)

10.1080/01969722.2011.600651

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Citations (9)

Pattern matching with wildcards and gap-length constraints based on a centrality-degree graph

Applied Intelligence (2012)

Dan Guo Xuegang Hu Fei Xie Xindong Wu

Degree (music)

10.1007/s10489-012-0394-4

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Citations (29)

Mining Frequent Patterns with Gaps and One-Off Condition

Yong‐Ming Huang Xindong Wu Xuegang Hu Fei Xie Jun Gao

Mining frequent patterns with a gap requirement from sequences is an important step in many domains, such as biological sciences. Given a character sequence S of length L, a certain threshold and a gap constraint, we aim to discover frequent patterns whose supports in S are no less than the given threshold value. A frequent pattern P can have wildcards, and the numbers of the wildcards between elements of P must fulfill user-specified gap constraints. Also, this mining process satisfies the one-off condition and an apriori-like property to be efficient. Experiments show that our method can mine as many frequent patterns with wildcards as the existing MPP algorithm, but has a much better performance in time.

10.1109/cse.2009.160

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Citations (7)

Privacy preserving attribute reduction for horizontally partitioned data

IEEE International Conference on Intelligent Systems and Knowledge Engineering (2010)

Mingquan Ye Xuegang Hu Changrong Wu

There has been concern over the apparent conflict between privacy and data mining. Attribute reduction is one of the most important contributions of rough set theory to data mining. In this paper, we address the issue of privacy preserving attribute reduction. Specifically, we consider a scenario in which two parties owning private data, wish to run a attribute reduction algorithm on the union of their databases, without revealing information about individuals. Our work is motivated by the need both to protect private information and to enable its use for research or other purposes. The above problem is a specific example of secure multi-party computation. We focus on the problem with the attribute reduction algorithm based on relative granularity, address an efficient protocol for securely computing the relative granularity, and present a privacy preserving attribute reduction algorithm for horizontally partitioned data.

Granularity

Private information retrieval

10.1109/iske.2010.5680856

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Citations (2)

Multi-scale information fusion generative adversarial network for real-world noisy image denoising

Machine Vision and Applications (2024)

Xuegang Hu Zhao Wei

Generative adversarial network

10.1007/s00138-024-01563-x

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Visual Processing by a Unified Schatten-p Norm and ℓ q Norm Regularized Principal Component Pursuit.

Jing Wang Meng Wang Xuegang Hu Shuicheng Yan

In this paper, we propose a non-convex formulation to recover the authentic structure from the corrupted real data. Typically, the specific structure is assumed to be low rank, which holds for a wide range of data, such as images and videos. Meanwhile, the corruption is assumed to be sparse. In the literature, such a problem is known as Robust Principal Component Analysis (RPCA), which usually recovers the low rank structure by approximating the rank function with a nuclear norm and penalizing the error by an `1-norm. Although RPCA is a convex formulation and can be solved effectively, the introduced norms are not tight approximations, which may cause the solution to deviate from the authentic one. Therefore, we consider here a non-convex relaxation, consisting of a Schatten-p norm and an `q-norm that promote low rank and sparsity respectively. We derive a proximal iteratively reweighted algorithm (PIRA) to solve the problem. Our algorithm is based on an alternating direction method of multipliers, where in each iteration we linearize the underlying objective function that allows us to have a closed form solution. We demonstrate that solutions produced by the linearized approximation always converge and have a tighter approximation than the convex counterpart. Experimental results on benchmarks show encouraging results of our approach.

Matrix norm

Rank (graph theory)

Low-rank approximation

Uniform norm

Source

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Classification model based on mutual information

Journal of Computer Applications (2011)

Xuegang Hu

Concerning the relevance between the attributes and the contribution difference of attribute values to attribute weights in classification dataset,an improved classification model and the formulas for calculating the impact factor and sample forecast information were proposed based on mutual information.And the classification model predicted the unlabelled object classes with the sample forecast information.Finally,the experimental results show that the classification model based on mutual information can effectively improve forecast precision and accuracy performance of classification algorithm.

Relevance

Sample (material)

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Citations (2)

A New Automatic Categorization Algorithm for Web Services

Jiaying He Peipei Li Xuegang Hu Xindong Wu

With the development of network technology, Web services are becoming more and more popular. The categorization of Web services is significant in many applications, such as service publishing, discovery and semantic annotation, and all existing categorization algorithms have inherent limitations. Therefore, a new automatic categorization algorithm, called NACWS, is presented in this paper, which takes advantage of existing algorithms for Web service categorization. Our algorithm of NACWS starts with traditional text categorization technology, and takes into account both the structure features and semantics to categorize Web services. Extensive experiments demonstrate that NACWS performs with a higher precision and a better recall on the categorization of Web services in comparison with several state-of-the-art categorization algorithms for Web services.

Text Categorization

10.1109/grc.2010.124

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Citations (4)

Learning shared and non-redundant label-specific features for partial multi-label classification

Information Sciences (2023)

Yizhang Zou Xuegang Hu Peipei Li Yuhang Ge

Feature (linguistics)

Multi-label classification

Feature Learning

10.1016/j.ins.2023.119917

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