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IEEE International Conference on Data Mining (ICDM)

作者： Changying Du Fuzhen Zhuang Qing He Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Multi-task learning has proven to be useful to boost the learning of multiple related but different tasks. Meanwhile, latent semantic models such as LSA and LDA are popular and effective methods to extract discriminative semantic features of high dimensional dyadic data. In this paper, we present a method to combine these two techniques together by introducing a new matrix tri-factorization based formulation for semi-supervised latent semantic learning, which can incorporate labeled information into traditional unsupervised learning of latent semantics. Our inspiration for multi-task semantic feature learning comes from two facts, i.e., 1) multiple tasks generally share a set of common latent semantics, and 2) a semantic usually has a stable indication of categories no matter which task it is from. Thus to make multiple tasks learn from each other we wish to share the associations between categories and those common semantics among tasks. Along this line, we propose a novel joint Nonnegative matrix tri-factorization framework with the aforesaid associations shared among tasks in the form of a semantic-category relation matrix. Our new formulation for multi-task learning can simultaneously learn (1) discriminative semantic features of each task, (2) predictive structure and categories of unlabeled data in each task, (3) common semantics shared among tasks and specific semantics exclusive to each task. We give alternating iterative algorithm to optimize our objective and theoretically show its convergence. Finally extensive experiments on text data along with the comparison with various baselines and three state-of-the-art multi-task learning algorithms demonstrate the effectiveness of our method.

关键词： Semantics Optimization Feature extraction Data mining Data models Joints Iterative methods

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Mining association rules on qing court medical records: Semantic abstraction and standardization

Mining association rules on qing court medical records: Sema...

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2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012

作者： Cao, Cong Wang, Weimin Cao, Cungen Zang, Liangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9780769548586

To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules. © 2012 IEEE.

关键词： Association rules

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A Cellular-Assisted Efficient Handover Algorithm for Wireless Sensor Networks

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International Journal of Communications, Network and System Sciences 2012年第10期5卷 708-713页

作者： Jun Zhu Hui Gao Yuling Ouyang Key Laboratory of Intelligent Computing & Singnal Processing Ministry of Education Anhui University Hefei China Shanghai Research Center for Wireless Communications Shanghai China

Heterogeneous network convergence and handover have become very hot in recent years. This paper proposed an efficient handover scheme in Multi-PAN Wireless Sensor Networks (WSNs). A number of edge nodes are set at the edge of each Personal Area Networks (PANs). A user equipment (UE), which has WSN and cellular network interface, acts as sensor node or mobile cluster head in WSN area. Thus, edge early warning can be acquired from edge nodes and neighbor channel information can be acquired with BS-assistance. Simulation results show that low transmission interrupted delay and low energy consumption can be achieved compared with conventional scheme in WSN.

关键词： Efficient Handover Edge Early Warning Special Beacon Wireless Sensor Networks

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Optic disc localization by projection with vessel distribution and appearance characteristics

Optic disc localization by projection with vessel distributi...

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International Conference on Pattern Recognition

作者： Dongbo Zhang Yao Yi Xingyu Shang Yinghui Peng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education The College of Information Engineering Xiangtan University China

ISBN: (纸本)9781467322164

Considering the vessel distribution and optic disc (OD) appearance characteristics comprehensively, a novel OD localization method based on 1-D projection is proposed. The horizontal location is determined by vascular scatter degree, an evaluation index of vessel distribution. And the vertical location is found by brightness and edge gradient around OD. The proposed method was tested on four publicly-available databases and a self-selection database. The OD was successfully located in 357 images out of 380 images (94%). And the proposed method shows good robustness in both normal and diseased images.

关键词： Databases Retina Optical imaging Biomedical optical imaging Optical filters Optical scattering Image segmentation

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

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7th IFIP International Conference on intelligent Information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information processing.

关键词： MapReduce

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Multi-label image annotation based on neighbor pair correlation chain 1

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8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012

作者： Jiang, Guang Liu, Xi Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Information Technology Laboratory Fujitsu Research and Development Center Co. Ltd. Beijing 100025 China

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Image annotation plays an important role in content-based image understanding, various machine learning methods have been proposed to solve this problem. In this paper, label correlation is considered as an undirected bipartite graph, in which each label are correlated by some common hidden topics. As a result, given a label, random walk with restart on the graph supplies a most related label, repeating this precedure leads to a label chain, which keep each adjacent labels pair correlated as maximally as possible. We coordinate the labels chain with its respective classifier training on bottom feature, and guide a classifier chain to annotate an image. The experiment illustrates that our method outperform both the baseline and another popular method. © 2012 Springer-Verlag.

关键词： Learning systems

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Parallel Web Mining System Based on Cloud Platform

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ZTE Communications 2012年第4期10卷 45-53页

作者： Shengmei Luo Qing He Lixia Liu Xiang Ao Ning Li Fuzhen Zhuang Pre-Research department of ZTE Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients, Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load （ETL） and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.

关键词： web mining large scale high volume high dimension cloudcomputing

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Numerical Methods For Fractional Variational Problems Depending On Indefinite Integrals

Numerical Methods For Fractional Variational Problems Depend...

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第五届国际自动控制联合会分数阶导数及其应用会议

作者： Dongling Wang Aiguo Xiao Hunan Key Laboratory for Computation and Simulation in Scienceand Engineering Key Laboratory of Intelligent Computing &Information Processing of Ministry of Education University XiangtanHunan P.R.China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education University Xiangtan Hunan P.R.China

In this paper,the fractional variational integrators for fractional variational problems depending on indefinite integrals in terms of Caputo derivative are *** corresponding fractional discrete Euler-Lagrange equatio... 详细信息

关键词： Numerical Methods For Fractional Variational Problems Depending On Indefinite Integrals

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Weakening interaction suppresses spontaneous symmetry breaking in two-channel asymmetric exclusion processes

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Physical Review E 2012年第4期85卷 041132-041132页

作者： Kai-Xuan Zhu Nian Wang Qing-Yi Hao Qi-Yi Liu Rui Jiang Key Laboratory of Intelligent Computing and Signal Processing of Education Ministry Anhui University Hefei 230039 China School of Engineering Science University of Science and Technology of China Hefei 230026 China School of Mathematics and Computational Science Anqing Teachers College Anqing 246133 China

This paper has studied spontaneous symmetry breaking (SSB) phenomenon in two types of two-channel asymmetric simple exclusion processes (ASEPs). One common feature of the two systems is that interactions for each species of particle happen at only one site, and the system reduces to two independent ASEPs when interaction vanishes. It is shown that with the weakening of interaction, the SSB is suppressed. More interestingly, the SSB disappears before the interaction is eliminated. Our work thus indicates that local interaction has to be strong enough to produce SSB. The mean-field analysis has been carried out, and the results are consistent with the simulation ones.

关键词： BROKEN symmetry (Physics) MEAN field theory STOCHASTIC processes SIMULATION methods & models STATISTICAL mechanics SYSTEMS theory

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