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Semi-supervised learning with local and global consistency by geodesic distance and sparse representation 1

3rd Sino-Foreign-Interchange Workshop on intelligent Science and intelligent Data Engineering, IScIDE 2012

作者： Gui, Jie Zhao, Zhongqiu Hu, Rongxiang Jia, Wei Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei Anhui 230031 China State Key Laboratory for Novel Software Technology Nanjing University China College of Computer Science and Information Engineering Hefei University of Technology China Institue of Energy Safety Chinese Academy of Sciences Hefei Anhui 230031 China

ISBN: (数字)9783642366697

ISBN: (纸本)9783642366680

In many practical data mining applications, such as web categorization, key gene selection, etc., generally, unlab.led training examples are readily availab.e, but lab.led ones are fairly expensive to obtain. Therefore, in recent years, semi-supervised learning algorithms such as graph-based methods have attracted much attention. However, most of these traditional methods adopted a Gaussian function to calculate the edge weights of the graph. In this paper, a novel weight for semi-supervised graph-based methods is proposed. The new method adds the lab.l information from problem into the target function, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting the calculation. In addition, we also add class prior knowledge from problem into semi-supervised learning algorithm. Here we address the problem of learning with local and global consistency (LGC). It was found that the effect of class prior knowledge was probably different between under high lab.l rate and low lab.l rate. Furthermore, we integrate sparse representation (SR) in LGC algorithm. Experiments on a UCI data set show that our proposed method outperforms the original algorithms. © Springer-Verlag 2013.

关键词： Learning algorithms

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EMPLOYING PLSA MODEL AND MAX-BISECTION FOR REFINING IMAGE ANNOTATION

EMPLOYING PLSA MODEL AND MAX-BISECTION FOR REFINING IMAGE AN...

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IEEE International Conference on Image processing

作者： Dongping Tian Wenbo Zhang Xiaofei Zhao Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479923427

We present a new method for refining image annotation by fusing probabilistic latent semantic analysis (PLSA) with max-bisection (MB). We first construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotating keyword for an image, and then a lab.l similarity graph is built by a weighted linear combination of lab.l similarity and visual similarity. Followed by the rank-two relaxation heuristics over the constructed lab.l graph is employed to further mine the correlation of the keywords so as to capture the refining annotation, which plays a critical role in semantic based image retrieval. The novelty of our method mainly lies in two aspects: exploiting PLSA to accomplish the initial semantic annotation task and implementing max-bisection based on the rank-two relaxation algorithm over the weighted lab.l graph to refine the candidate annotations generated by the PLSA. We evaluate our method on the standard Corel dataset and the experimental results are competitive to several state-of-the-art approaches.

关键词： Refining image annotation PLSA EM max-bisection image retrieval image retrieval images ANNOTATIONS Refining lab.lled graph Line graph Linear Combination Purification keyWORD

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Security games with surveillance cost and optimal timing of attack execution

Security games with surveillance cost and optimal timing of ...

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12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

作者： An, Bo Brown, Matthew Vorobeychik, Yevgeniy Tambe, Milind Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Southern California Los Angeles CA 90089 United States Sandia National Laboratories Livermore CA 94550 United States

ISBN: (纸本)9781627487665

Stackelberg games have been used in several deployed applications to allocate limited resources for critical infrastructure protection. These resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. Past work has typically assumed that the attacker has perfect knowledge of the defender's randomized strategy or can learn the defender's strategy after conducting a fixed period of surveillance. In consideration of surveillance cost, these assumptions are clearly simplistic since attackers may act with partial knowledge of the defender's strategies and may dynamically decide whether to attack or conduct more surveillance. In this paper, we propose a natural model of limited surveillance in which the attacker dynamically determine a place to stop surveillance in consideration of his updated belief based on observed actions and surveillance cost. We show an upper bound on the maximum number of observations the attacker can make and show that the attacker's optimal stopping problem can be formulated as a finite state space MDP. We give mathematical programs to compute optimal attacker and defender strategies. We compare our approaches with the best known previous solutions and experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker's optimal stopping decision into account, validating the motivation of our work. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Game theory

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Exploit spatial relationships among pixels for saliency region detection using topic model

Exploit spatial relationships among pixels for saliency regi...

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19th International Conference on Advances in Multimedia Modeling, MMM 2013

作者： Jiang, Guang Liu, Xi Yue, JinPeng 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 Fujitsu Research and Development Center Co. LTD Beijing 100025 China

ISBN: (纸本)9783642357244

In this paper, we describe an approach to saliency detection as a two-category (salient or not) soft clustering using topic model. In order to simulate human's paralleled visual neural perception, many subregions are sampling from an image, where each one is considered as a set of colors from a codebook, which is a color palette for the image. We assume salient pixels would appear spatial adjacent more possibly, therefore in a same sub-region, while less salient pixels would either. Consequently, all the sub-regions are clustered into two assumed topics with probabilities: "salient"/ "non-salient", while "salient" one is decided to give saliency value of each pixel according to its posterior conditional probability. Our method will give a global saliency map with full resolution, and experiments illustrate it is competitive with the state-of-art methods. © Springer-Verlag 2013.

关键词： Pixels

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Preface: WCSE 2013

Proceedings - 2013 4th World Congress on Software Engineerin...

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Proceedings - 2013 4th World Congress on Software Engineering, WCSE 2013 2013年 xi-xii页

作者： Xu, Li Da Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology China University of Mining and Technology China

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A Distributed 2D-to-3D Video Conversion System

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China Communications 2013年第5期10卷 30-38页

作者：张哲斌张吉安张学西王亦洲高文 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences National Engineering Laboratory for Video Technology and Key Laboratory of Machine Perception (Ministry of Education) School of Electronics Engineering and Computer Science Peking University University of Chinese Academy of Sciences

2D-to-3D video conversion is a feasible way to generate 3D programs for the current 3DTV industry. However, for large-scale 3D video production, current systems are no longer adequate in terms of the time and lab.r required for conversion. In this paper, we introduce a distributed 2D-to-3D video conversion system that includes a 2D-to-3D video conversion module, architecture of the parallel computation on the cloud, and 3D video coding in the system. The system enables cooperation among multiple users in the simultaneous completion of their conversion tasks so that the conversion efficiency is greatly promoted. In the experiments, we evaluate the system based on criteria related to both time consumption and video coding performance.

关键词： 3D video 2D-to-3D conversion distributed system

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The Sound and Complete <i>R</i>-Calculi with Respect to Pseudo-Revision and Pre-Revision

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International Journal of Intelligence Science 2013年第2期3卷 110-117页

作者： Wei Li Yuefei Sui Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astronautics Beijing China

The AGM postulates ([1]) are for the belief revision (revision by a single belief), and the DP postulates ([2]) are for the iterated revision (revision by a finite sequence of beliefs). Li [3] gave an R-calculus for R-configurations △|Γ, where Δ is a set of literals, and Γ is a finite set of formulas. We shall give two R-calculi such that for any consistent set Γ and finite consistent set △ of formulas in the propositional logic, in one calculus, there is a pseudo-revision Θ of Γ by Δ such that is provable and and in another calculus, there is a pre-revision Ξ of Γ by Δ such that is provable, and for some pseudo-revision Θ;and prove that the deduction systems for both the R-calculi are sound and complete with the pseudo-revision and the pre-revision, respectively.

关键词： Belief Revision R-Calculus Maximal Consistent Set Pseudo-Revision Pre-Revision

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A novel neural network classification model based on covering and Affinity Propagation clustering algorithm

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Journal of Computational information Systems 2013年第7期9卷 2565-2573页

作者： Li, Hui Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Jiangsu Normal University Xuzhou 221116 China

Affinity Propagation (AP) algorithm can automatically determine the cluster center and does not need a pre-determined number of clustering. This paper presents a novel neural network classification model, using AP clustering method as a pre-processor to cluster high dimensional massive data samples, and combining covering algorithm to automatically determine the center of the covering, and calculating its radius, and then converting these parameters into the weights and thresholds of the hidden layer of classification neural network. The number of hidden layer neurons is the number of covering. The output layer is the M-P neuron layer. By evaluating the input feature of hidden layer to complete the mapping from covering to the output, and gain the corresponding output category of the input feature. Simulation results show that our proposed neural network classification model can get better performance. Copyright © 2013 Binary information Press.

关键词： Clustering algorithms

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Converting continuous-space language models into N-gram language models for statistical machine translation

Converting continuous-space language models into N-gram lang...

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

作者： Wang, Rui Utiyama, Masao Goto, Isao Sumita, Eiichro Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai200240 China

ISBN: (纸本)9781937284978

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, CSLMs have not been used in the first pass decoding of SMT, because using CSLMs in decoding takes a lot of time. In contrast, we propose a method for converting CSLMs into back-off n-gram language models (BNLMs) so that we can use converted CSLMs in decoding. We show that they outperform the original BNLMs and are comparable with the traditional use of CSLMs in reranking. © 2013 Association for Computational Linguistics.

关键词： Decoding

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One Sound and Complete <i>R</i>-Calculus with Pseudo-Subtheory Minimal Change Property

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Journal of Computer and Communications 2013年第5期1卷 20-25页

作者： Wei Li Yuefei Sui Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. State Key Laboratory of Software Development Environment Beijing University of Aeronautics and Astronautics Beijing China

The AGM axiom system is for the belief revision (revision by a single belief), and the DP axiom system is for the iterated revision (revision by a finite sequence of beliefs). Li [1] gave an R-calculus for R-configurations Δ|Γ,?where?Δ?is a set of atomic formulas or the negations of atomic formulas, and?Γ?is a finite set of formulas. In propositional logic programs, one R-calculus N will be given in this paper, such that N is sound and complete with respect to operator s(Δ,t), where s(Δ,t)is a pseudo-theory minimal change of t by?Δ.

关键词： Belief Revision R-Calculus Soundness and Completeness of a Calculus Pseudo-Subtheory

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