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Tumor gene expressive data classification based on locally linear representation Fisher criterion 1

9th International Conference on intelligent Computing, ICIC 2013

作者： Li, Bo Tian, Bei-Bei Liu, Jin School of Computer Science and Technology Wuhan University of Science and Technology 430081 Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China State Key Lab. of Software Engineering Wuhan China

ISBN: (数字)9783642394829

ISBN: (纸本)9783642394812

In this paper, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene expressive data. In the proposed LLRFC, an inter-class graph and intra-class graph is constructed based on the class information of tumor gene expressive data, where the weights between nodes in both graph are optimized using locally linear representation trick. Moreover, a Fisher criterion is modeled to maximize the inter-class scatter and minimize the intra-class scatter simultaneously. Experiments on some benchmark tumor gene expressive data validate its efficiency. © 2013 Springer-Verlag.

关键词： Genes

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A modified fuzzy c-means algorithm brain MR images segmentation with bias field compensation

A modified fuzzy c-means algorithm brain MR images segmentat...

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3rd International Conference on Materials Science and information technology, MSIT 2013

作者： Liu, Xiao Li Guo, Yu Ting Kong, Jun Wang, Jian Zhong College of Computer Science and Information Technology Northeast Normal University Changchun China Key Laboratory of Intelligent Information Processing of Jilin Universities Northeast Normal University Changchun China National Engineering Laboratory for Druggable Gene and Protein Screening Northeast Normal University Changchun China

ISBN: (纸本)9783037857700

Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods. © (2013) Trans Tech Publications, Switzerland.

关键词： Image segmentation

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Adaptive Particle Filter with Estimation Windows

Adaptive Particle Filter with Estimation Windows

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2013年中国智能自动化学术会议

作者： Peng Li Jian Tang College of Information and Technology Key Laboratory of Intelligent Computing and Information ProcessingMinistry of EducationXiangtan University

Particle filter is well suited to estimate the state of non-linear non-Gaussian dynamic systems,which comes at the cost of higher computational *** in many real time applications,it must deal with constraints imposed by limited computational *** deal with this question,we distribute the samples among the different observations arriving during a filter update, the novel algorithm represents densities over the state space by mixtures of sample *** contribution of this paper is to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation *** to the relative entropy theory and particle number controller idea,we choose the number of samples,decrease computation overhead.A simulation of the classic HARD bearing only tracking problem is presented,the results show that the novel algorithm performs better than generic particle filter.

关键词： Particle filter Estimation window Relative entropy Particle number controller

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Enhancing ability of fault detection for component systems based on object interactions graph

Enhancing ability of fault detection for component systems b...

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2012 International Conference on Emerging Technologies for information Systems, Computing, and Management, ICM 2012

作者： Sun, Fuzhen Liao, Lejian Du, Jianguang Li, Guoqiang Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications Beijing Key Laboratory of Intelligent Information Technology School of Computer Science Beijing China School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (纸本)9781461470090

Test case prioritization is a technique to schedule the test case in order to maximize some objective function. Early fault detection can provide a faster feedback generating a scope for debuggers to carry out their task at an early stage. In this paper, a method is proposed to prioritize the test cases for testing component dependency in a Component Based Software Development (CBSD) environment using Greedy Approach. The OIG (Object Interaction Graph) is traversed to calculate the total number of inter component object interactions and intra component object interactions. Depending upon the number of interactions, the objective function is calculated and the test cases are ordered accordingly. This technique is applied to the components developed in Java for a software system and found to be very effective in early fault detection as compared with non-prioritize approach. © 2013 Springer Science+Business Media New York.

关键词： Fault detection

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Translation with source constituency and dependency trees

Translation with source constituency and dependency trees

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2013 Conference on Empirical Methods in Natural Language processing, EMNLP 2013

作者： Meng, Fandong Xie, Jun Song, Linfeng Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland

ISBN: (纸本)9781937284978

We present a novel translation model, which simultaneously exploits the constituency and dependency trees on the source side, to combine the advantages of two types of trees. We take head-dependents relations of dependency trees as backbone and incorporate phrasal nodes of constituency trees as the source side of our translation rules, and the target side as strings. Our rules hold the property of long distance reorderings and the compatibility with phrases. Large-scale experimental results show that our model achieves significantly improvements over the constituency-to-string (+2.45 BLEU on average) and dependency-to-string (+0.91 BLEU on average) models, which only employ single type of trees, and significantly outperforms the state-of-the-art hierarchical phrase-based model (+1.12 BLEU on average), on three Chinese-English NIST test sets. © 2013 Association for Computational Linguistics.

关键词： Forestry

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Embedding with autoencoder regularization

Embedding with autoencoder regularization

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

作者： Yu, Wenchao Zeng, Guangxiang Luo, Ping Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China University of Science and Technology of China China HP Labs. China China

ISBN: (纸本)9783642409936

The problem of embedding arises in many machine learning applications with the assumption that there may exist a small number of variabilities which can guarantee the "semantics" of the original high-dimensional data. Most of the existing embedding algorithms perform to maintain the locality-preserving property. In this study, inspired by the remarkable success of representation learning and deep learning, we propose a framework of embedding with autoencoder regularization (EAER for short), which incorporates embedding and autoencoding techniques naturally. In this framework, the original data are embedded into the lower dimension, represented by the output of the hidden layer of the autoencoder, thus the resulting data can not only maintain the locality-preserving property but also easily revert to their original forms. This is guaranteed by the joint minimization of the embedding loss and the autoencoder reconstruction error. It is worth mentioning that instead of operating in a batch mode as most of the previous embedding algorithms conduct, the proposed framework actually generates an inductive embedding model and thus supports incremental embedding efficiently. To show the effectiveness of EAER, we adapt this joint learning framework to three canonical embedding algorithms, and apply them to both synthetic and real-world data sets. The experimental results show that the adaption of EAER outperforms its original counterpart. Besides, compared with the existing incremental embedding algorithms, the results demonstrate that EAER performs incremental embedding with more competitive efficiency and effectiveness. © 2013 Springer-Verlag.

关键词： Semantics

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Iterative transformation of annotation guidelines for constituency parsing

Iterative transformation of annotation guidelines for consti...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Li, Xiang Jiang, Wenbin Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Centre for Next Generation Localisation Faculty of Engineering and Computing Dublin City University Ireland

ISBN: (纸本)9781937284510

This paper presents an effective algorithm of annotation adaptation for constituency treebanks, which transforms a treebank from one annotation guideline to another with an iterative optimization procedure, thus to build a much larger treebank to train an enhanced parser without increasing model complexity. Experiments show that the transformed Tsinghua Chinese Treebank as additional training data brings significant improvement over the baseline trained on Penn Chinese Tree-bank only. © 2013 Association for Computational Linguistics.

关键词： Iterative methods

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A heterogeneous-aware cooperative MIMO transmission scheme in WSN

Journal of Software

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Journal of Software 2013年第3期8卷 639-644页

作者： Pei, Tingrui Xie, Da Li, Zhetao Tu, Dengbiao Choi, Youngjune College of Information Engineering Xiangtan University Xiangtan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China School of Computer National University of Defense Technology Changsha 410073 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Department of Information and Computer Engineering Ajou University Suwon 443749 Korea Republic of

A heterogeneous-aware cooperative MIMO transmission scheme (HAMS) is proposed to optimize the network lifetime and save energy for energy heterogeneous wireless sensor networks (WSN). This scheme extends the traditional low-energy adaptive clustering hierarchy (LEACH) protocol to enable the cooperative MIMO transmission between the sink and clusters. Through the adaptive selection of cooperative nodes and the cooperative MIMO transmission, HAMS can gain effective performance improvement in terms of taking advantage of the presence of node heterogeneity. Based on the energy consumption model developed in this paper, the optimal parameter to minimize the overall energy consumption is found. Simulation results exhibit that HAMS can effectively save energy and prolong the network time. Furthermore, HAMS displays more resilient in different degree of heterogeneous wireless sensor networks. © 2013 ACADEMY PUBLISHER.

关键词： Energy utilization

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Distributed query processing in an ad-hoc semantic web data sharing system

Distributed query processing in an ad-hoc semantic web data ...

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2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Zhou, Jing Bochmann, Gregor V. Shi, Zhongzhi School of Computer Science Communication University of China Beijing China School of Electrical Engineering and Computer Science University of Ottawa ON Canada Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769549798

Sharing the Semantic Web data in proprietary datasets in which data is encoded in RDF triples in a decentralized environment calls for efficient support from distributed computing technologies. The highly dynamic ad-hoc settings that would be pervasive for Semantic Web data sharing among personal users in the future, however, pose even more demanding challenges for the enabling technologies. We extend previous work on a hybrid P2P architecture for an ad-hoc Semantic Web data sharing system which better models the data sharing scenario by allowing data to be maintained by its own providers and exhibits satisfactory scalability owing to the adoption of a two-level distributed index and hashing techniques. Additionally, we propose efficient distributed processing of SPARQL queries in such a context and explore optimization techniques that build upon distributed query processing for database systems and relational algebra optimization. We anticipate that our work will become an indispensable, complementary approach to making the Semantic Web a reality by delivering efficient data sharing and reusing in an ad-hoc environment. © 2013 IEEE.

关键词： Query processing

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Classification of big velocity data via cross-domain Canonical Correlation Analysis

Classification of big velocity data via cross-domain Canonic...

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IEEE International Conference on Big Data

作者： Bo Zhang Zhong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Many classification techniques work well only under a common assumption that the training and test data are drawn from the same feature space and the same distribution. However, big velocity data usually show disobedience of this assumption. For example, in the field of web-document classification, new document is continuously emerging every day. Transfer learning aims at leveraging the knowledge in labeled source domains to predict the unlabeled data in a target domain, where the distributions are different in domains. As one of the important research directions of transfer learning, one kind of approaches focus on the correspondence between pivot features and all the other specific features from different domains, to extract some relevant features that may reduce the difference between the domains, have attracted wide attention and study. However, the limitation caused by the vague meanings in different domains prevents these algorithms from further improvement. To tackle this problem, we propose a cross-domain canonical correlation analysis algorithm called CD-CCA by applying Canonical Correlation Analysis (CCA) to transfer learning. CD-CCA can learn a semantic space of multi-view correspondences from different domains respectively and transfer the knowledge by dimensionality reduction in a multi-view way. Experimental results on the 144×6 classification problems in 20Newsgroups, show that CD-CCA can significantly improve the prediction accuracy.

关键词： Correlation Algorithm design and analysis Classification algorithms Feature extraction Semantics Vectors Mutual information

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