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Learning robust and discriminative low-rank representations for face recognition with occlusion

Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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Provable Secure Node Authentication Protocol for Wireless Sensor Networks 13

Provable Secure Node Authentication Protocol for Wireless Se...

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13th Web information Systems and Applications Conference, WISA 2016

作者： Zhimeng, Liu Yanli, Zhao Key Laboratory of Intelligent Information Processing in Universities of Shandong Shandong Institute of Business and Technology Yantai China School of Computer Science and Technology Shandong Institute of Business and Technology Yantai China

ISBN: (纸本)9781509054374

To protect information or messages transmitted between any two adjacent sensor nodes from variety of malicious attacks, a CK secure authentication and key exchange protocol for Wireless Sensor Networks is proposed. Security of the protocol, which has security properties including resistance to key compromise impersonation attacks, relies on Computational Diffie-Hellman Problem. Because of low communication overhead, the scheme is better suited for Wireless Sensor Networks to establish the session key between two adjacent nodes. © 2016 IEEE.

关键词： Sensor nodes

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Efficient mining Algorithm of frequent itemsets for uncertain data streams 9

Efficient mining Algorithm of frequent itemsets for uncertai...

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9th International Symposium on Computational Intelligence and Design, ISCID 2016

作者： Qianqian, Wang Fang-Ai, Liu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9781509035588

With the rapid development of computer technology, web services has been widely used. In these applications, the uncertain data is in the form of streams. In view of this kind of situation, present a new generalized data structure, that is, PSUF - tree, to store uncertain data streams, all itemsets in recent window are contained in global PStree in a condensed format, establish a header table in which contains dynamic array of expected value whose header table saved the same itemsets. Based on PSUF-tree, present a new mining algorithm for frequent itemsets, that is, PSUF-streaming algorithm, frequent itemsets could be mined by traversing the dynamic array, the maintaining of PSUF-tree just handles the header table corresponds to the oldest batch of itemsets in window. The experimental results show that PSUF-streaming algorithm has good efficiency and scalability, and reduce memory usage to some extent. © 2016 IEEE.

关键词： Data mining

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The constitution of a fine-grained opinion annotated corpus on Weibo 15th

The constitution of a fine-grained opinion annotated corpus ...

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15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language processing Based on Naturally Annotated Big Data, NLP-NABD 2016

作者： Jian, Liao Yang, Li Suge, Wang School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China

ISBN: (纸本)9783319476735

Sentiment analysis on social media represented by Weibo is one of the hotspot research problems in NLP. A comprehensive and systematic fine-grained annotated corpus plays a significance role. In this paper, considering the characteristics of Weibo, we focus on the constitution of a fine-grained, hierarchical opinion annotated corpus and design a set of labelling specification. We manually annotate the opinion sentences with a part of ones containing hidden opinion which can be useful for implicit sentiment analysis. Then a fine-grained aspect extraction, namely opinion triples like is finished for aspect-level sentiment research. Moreover, we establish an evaluation method for the task of fine-grained aspect extraction which has been applied in evaluation for years. The corpus was used in the task of COAE2015, and it will be a useful resource for the related research on social media sentiment analysis. © Springer International Publishing AG 2016.

关键词： Sentiment analysis

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Automatic cross-lingual similarization of dependency grammars for tree-based machine translation

Automatic cross-lingual similarization of dependency grammar...

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Jiang, Wenbin Zhang, Wen Xu, Jinan Cai, Rangjia Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China Research Center of Tibetan Information Qinghai Normal University China

ISBN: (纸本)9781945626258

Structural isomorphism between languages benefits the performance of cross-lingual applications. We propose an automatic algorithm for cross-lingual similarization of dependency grammars, which automatically learns grammars with high cross-lingual similarity. The algorithm similarizes the annotation styles of the dependency grammars for two languages in the level of classification decisions, and gradually improves the cross-lingual similarity without losing linguistic knowledge resorting to iterative cross-lingual cooperative learning. The dependency grammars given by cross-lingual similarization have much higher cross-lingual similarity while maintaining non-triviality. As applications, the cross-lingually similarized grammars significantly improve the performance of dependency tree-based machine translation. © 2016 Association for Computational Linguistics

关键词： Machine translation

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Hyperspectral light field stereo matching

arXiv

引用

arXiv 2017年

作者： Zhu, Kang Xue, Yujia Fu, Qiang Kang, Sing Bing Chen, Xilin Yu, Jingyi School of Information Science and Technology ShanghaiTech University Shanghai China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Plex-VR Inc. Shanghai Microsoft Research RedmondWA United States Department of Computer and Information Sciences University of Delaware NewarkDE United States

In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 × 6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues that rely on BWNCC. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses. Copyright © 2017, The Authors. All rights reserved.

关键词： Cameras

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Soft margin consistency based scalable multi-view maximum entropy discrimination 25

Soft margin consistency based scalable multi-view maximum en...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Mao, Liang Sun, Shiliang Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 500 Dongchuan Road Shanghai200241 China

Multi-view learning receives increasing interest in recent years to analyze complex data. Lately, multiview maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) were proposed as extensions of maximum entropy discrimination (MED) to the multi-view learning setting, which use the hard margin consistency principle that enforces two view margins to be the same. In this paper, we propose soft margin consistency based multi-view MED (SMVMED) achieving margin consistency in a less strict way, which minimizes the relative entropy between the posteriors of two view margins. With a trade-off parameter balancing large margin and margin consistency, SMVMED is more flexible. We also propose a sequential minimal optimization (SMO) algorithm to efficiently train SMVMED and make it scalable to large datasets. We evaluate the performance of SMVMED on multiple real-world datasets and get encouraging results.

关键词： Optimization

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Active transfer learning of matching query results across multiple sources

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Frontiers of computer Science 2015年第4期9卷 595-607页

作者： Jie XIN Zhiming CUI Pengpeng ZHAO Tianxu HE The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Entity resolution （ER） is the problem of identi- fying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under super- vised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Al- though such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sam- piing strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classi- fiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our exper- imental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewerlabeled samples for record matching with numerous and var- ied sources.

关键词： entity resolution active learning transfer learn-ing convex optimization

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A rendezvous node selection algorithm for mobile sink path planning in wireless sensor networks 6

A rendezvous node selection algorithm for mobile sink path p...

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2016 6th International Workshop on computer Science and Engineering, WCSE 2016

作者： Wu, Xiaoming Wang, Yinglong Hu, Yifan College of Information Science and Engineering Shandong University of Science and Technology Qingdao China Shandong Computer Science Center Jinan China Shandong Provincial Key Laboratory of Computer Network Jinan China

ISBN: (纸本)9789811100086

Recent research shows that significant energy hole problem can be solved in wireless sensor network (WSN) by using mobile sink capable of carrying data. In order to plan an optimal moving tour for mobile sink without long distance travel, we develop the rendezvous node selection algorithm (RNSA) to find rendezvous nodes (RN) for the sink to visit. The RNSA can select the set of RNs to act as store points for the mobile sink, and several factors would be considered to find suitable RNs from sensor nodes, the other sensor nodes would forward data via multi hopping to the nearest RN, such that the energy consumption of sensor nodes is minimized and uniform to avoid the problem of energy holes while ensuring sensed data are collected on time. Our scheme is then validated through extensive simulations.

关键词： Sensor nodes

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Ensemble feature selection integrating elitist roles and quantum game model

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Journal of Systems Engineering and Electronics 2015年第3期26卷 584-594页

作者： Weiping Ding Jiandong Wang Zhijin Guan Quan Shi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nantong University Provincial Key Laboratory for Computer Information Processing Technology Soochow University

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

关键词： ensemble quantum game utility matrix of trust mar-gin dynamics equilibrium strategy multilevel elitist role feature selection and classification.

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