Xiaodi Huang

Publications (64) View all

• Source

  Available from: Xiaodi Huang

  Chapter: WSRANK: A NEW ALGORITHM FOR RANKING WEB SERVICES

  Xiaodi Huang
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  ABSTRACT: Web services are one of important components in the service-oriented computing paradigm. As the increasing number of Web services is available for service requests, it is demand to develop an algorithm for ranking them with respect to some criteria. In addition, the algorithm should be capable of taking into account the fact that the user requirements for a service may be diverse. This paper presents approaches for ranking Web services in different scenarios. Two algorithms are developed for this purpose
  02/2013: pages pp. 529-539;
• Source

  Available from: Xiaodi Huang

  Article: Clustering via geometric median shift over Riemannian manifolds

  Yang Wang, Xiaodi Huang, Lin Wi
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  ABSTRACT: The mean shift algorithms have been successfully applied to many areas, such as data clustering, feature analysis, and image segmentation. However, they still have two limitations. One is that they are ineffective in clustering data with low dimensional manifolds because of the use of the Euclidean distance for calculating distances. The other is that they sometimes produce poor results for data clustering and image segmentation. This is because a mean may not be a point in a data set. In order to overcome the two limitations, we propose a novel approach for the median shift over Riemannian manifolds that uses the geometric median and geodesic distances. Unlike the mean, the geometric median of a data set is one of points in the set. Compared to the Euclidean distance, the geodesic distances can better describe data points distributed on Riemannian manifolds. Based on these two facts, we first present a novel density function that characterizes points on a manifold with the geodesic distance. The shift of the geometric median over the Riemannian manifold is derived from maximizing this density function. After this, we present an algorithm for geometric median shift over Riemannian manifolds, together with theoretical proofs of its correctness. Extensive experiments have demonstrated that our method outperforms the state-of-the-art algorithms in data clustering, image segmentation, and noise filtering on both synthetic data sets and real image databases.
  Information Sciences 01/2013; 220:292-305. · 2.83 Impact Factor
• Article: A consensus method for prioritising drug-associated target proteins.

  Gang Shu, Xiaodi Huang, Shanfeng Zhu
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  ABSTRACT: It is generally believed that the degree of a relation between two entities is likely to be stronger if they co-occur more often in the literature. Based on this assumption, several methods are used in biomedical text mining such as support, confidence, chi-square, odds ratio, lift, all-confidence, coherence, and pof. Comparing these eight methods, our work aims to find the best one. Also, we present a consensus approach that can further improve the performance. Experimental results on prioritising drug targets have shown that pof, coherence, and all-confidence in sequence are the top three. By integrating coherence into pof, the consensus method is the best one among all compared methods.
  International Journal of Data Mining and Bioinformatics 01/2012; 6(2):178-95. · 0.43 Impact Factor
• Conference Proceeding: An attribute-based scheme for service recommendation using association rules and ant colony algorithm

  Zhikui Chen, Zhuang Shao, Zhijiang Xie, Xiaodi Huang
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  ABSTRACT: With the rapid development of m-commerce, predicting user's navigation and making the service recommendation become more and more important. Most researches focus on predicting user's navigation using context history and user preferences. But, the influence of the attributes of a service has been ignored. Simultaneously, some attributes are variable, so the recommendations are changeable. Therefore, the paper proposes an attribute-based scheme for service recommendation based on CASUP (context-aware system considering user preference). In proposed approach, the services are classified into several service clusters, and the service recommendations are carried out using Apriori algorithm and ant colony algorithm. Finally, the proposed model is validated by several simulation experiments, which demonstrate the effects of the service attributes in m-commerce.
  Wireless Telecommunications Symposium (WTS), 2010; 05/2010
• Conference Proceeding: Subspace intersection method of bearing estimation based on least square approach in shallow ocean

  Jincheng Pang, Jing Lin, Aimin Zhang, Xiaodi Huang
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  ABSTRACT: In this paper, least square approach is applied in subspace intersection (SI) method for the problem of bearing estimation in shallow water. Based on that, a method called constrained least square subspace intersection method (CLS-SI) is proposed. The mathematic expressions of CLS-SI are given. In addition, the relationship between CLS-SI and MUSIC is discussed. Simulations show that the performance of the new method proposed is better than that of the original SI method.
  Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on; 05/2008 · 4.63 Impact Factor