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Robust non-negative matrix factorization

Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 192-200页

作者： Lijun ZHANG Zhengguang CHEN Miao ZHENG Xiaofei HE Zhejiang Key Laboratory of Service Robot College of Computer ScienceZhejiang UniversityHangzhou 310027China State Key Laboratory of CAD&CG College of Computer ScienceZhejiang UniversityHangzhou 310058China

Non-negative matrix factorization(NMF)is a recently popularized technique for learning partsbased,linear representations of non-negative *** traditional NMF is optimized under the Gaussian noise or Poisson noise assumption,and hence not suitable if the data are grossly *** improve the robustness of NMF,a novel algorithm named robust nonnegative matrix factorization(RNMF)is proposed in this *** assume that some entries of the data matrix may be arbitrarily corrupted,but the corruption is *** decomposes the non-negative data matrix as the summation of one sparse error matrix and the product of two non-negative *** efficient iterative approach is developed to solve the optimization problem of *** present experimental results on two face databases to verify the effectiveness of the proposed method.

关键词： robust non-negative matrix factorization(RNMF) convex optimization dimensionality reduction

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R2: Incremental eprogramming using elocatable code in networked embedded systems

R2: Incremental eprogramming using elocatable code in networ...

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IEEE INFOCOM 2011

作者： Dong, Wei Liu, Yunhao Chen, Chun Bu, Jiajun Huang, Chao Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University China Department of Computer Science HKUST Hong Kong

ISBN: (纸本)9781424499212

We present R2, an incremental eprogramming approach using Relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. R2 achieves a higher degree of similarity than existing approaches by mitigating the effects of both function shifts and data shifts. R2 makes efficient use of memory and does not degrade program quality. It adopts an optimized differencing algorithm to generate small delta files for efficient dissemination. We demonstrate R2's advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale and long-term sensor systemGreenOrbs. Results show that R2 reduces the dissemination cost by approximately 65% compared to Deluge-state-of-the-art network reprogramming approach, and reduces the dissemination cost by approximately 20% compared to Zephyr and Hermes-the latest works on incremental reprogramming. © 2011 IEEE.

关键词： Embedded systems

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Social recommendation using low-rank semidefinite program 25

Social recommendation using low-rank semidefinite program

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25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

作者： Zhu, Jianke Ma, Hao Chen, Chun Bu, Jiajun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Microsoft Research United States

ISBN: (纸本)9781577355083

The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Laplace transforms

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ICAD: Indirect correlation based anomaly detection in dynamic WSNs

ICAD: Indirect correlation based anomaly detection in dynami...

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IEEE Conference on Wireless Communications and Networking

作者： Yi Gao Chun Chen Jiajun Bu Wei Dong Daojing He Zhejiang Key Laboratory of Service Robot College of Computer Science University of Zhejiang China

Anomaly detection is an essential functionality of Wireless Sensor Networks (WSNs) due to their complex behaviors and the wireless dynamics. In dynamic WSNs, many characteristics such as network topology, locations of sensor nodes, change frequently over time. We observe that indirect correlations among multiple attributes of a sensor node can be utilized to capture and model the historical behaviors. Prior studies overlooked indirect correlations while in this study we exploit it for detecting anomaly efficiently and accurately. Therefore, we propose ICAD, an indirect correlation based anomaly detection approach. By applying the Markov chain, the state transition probability matrix is calculated and it is subsequently used to detect anomalies. Compared to prior approaches, ICAD can detect different types of anomalies simultaneously. Furthermore, ICAD is implemented based on TinyOS and evaluated in a test-bed with 17 TelosB motes. Evaluation results show that ICAD has high detection accuracy with acceptable overhead.

关键词： Markov processes Correlation Wireless sensor networks Network topology Probability distribution Random access memory Training

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Jumping Scanning Path Error Diffusion: A Novel Halftoning Algorithm Improving Mid-tone Quality

Jumping Scanning Path Error Diffusion: A Novel Halftoning Al...

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Workshop on Digital Media and Digital Content Management (DMDCM)

作者： Yan Zhou Chun Chen Qiang Wang Jiajun Bu Zhejiang Key Laboratory of Service Robot College of Computer Science University of Zhejiang Hangzhou China

In this paper, we describe a novel error diffusion scheme for higher halftone quality with less visual artifacts. The proposed algorithm improves mid-tone quality of error diffusion significantly by diffusing the error along a jumping scanning path. The algorithm calculates the accumulative error to determine the point where breaks up the scanning path. A cost function is developed to search the optimal threshold of the accumulative error. The cost function helps to bring the Fourier spectra as close as possible to the corresponding "blue noise" spectra, and minimize the anisotropy. Experiments show that compared with existing error diffusion algorithms that use continuous scanning path, the new algorithm can remove visual anomalies more effectively. Fourier analysis of experimental results further supports this conclusion.

关键词： Pixel Noise Algorithm design and analysis Quantization Cost function Grippers Visualization

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ℛ2: Incremental ℛeprogramming using ℛelocatable code in networked embedded systems

ℛ2: Incremental ℛeprogramming using ℛelocatable code in n...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wei Dong Yunhao Liu Chun Chen Jiajun Bu Chao Huang Department of Computer Science HKUST China Zhejiang Key Laboratory of Service Robot College of Computer Science University of Zhejiang China

We present ℛ 2 , an incremental ℛeprogramming approach using Relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. ℛ 2 achieves a higher degree of similarity than existing approaches by mitigating the effects of both function shifts and data shifts. ℛ 2 makes efficient use of memory and does not degrade program quality. It adopts an optimized differencing algorithm to generate small delta files for efficient dissemination. We demonstrate ℛ 2 's advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale and long-term sensor system-GreenOrbs. Results show that ℛ 2 reduces the dissemination cost by approximately 65% compared to Deluge-state-of-the-art network reprogramming approach, and reduces the dissemination cost by approximately 20% compared to Zephyr and Hermes-the latest works on incremental reprogramming.

关键词： Wireless sensor networks Protocols Green products Embedded systems Ash Algorithm design and analysis Geophysical measurement techniques

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Exploiting concurrency for efficient dissemination in Wireless Sensor Networks

Exploiting concurrency for efficient dissemination in Wirele...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Yi Gao Jiajun Bu Wei Dong Chun Chen Lei Rao Xue Liu Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science University of Zhejiang China School of Computer Science McGill University Canada

Wireless Sensor Networks (WSNs) can be successfully applied in a wide range of applications. Efficient data dissemination is a fundamental service which enables many useful high-level functions such as parameter reconfiguration, network reprogramming, and etc. Many current data dissemination protocols employ network coding techniques to deal with packet losses. The coding overhead, however, becomes a bottleneck in terms of dissemination delay. We exploit the concurrency ability of sensor nodes and propose MT-Deluge, a multi-threaded design of a coding-based data dissemination protocol. By separating the coding and radio operations into two threads and carefully scheduling their executions, MT-Deluge shortens the dissemination delay effectively. An incremental decoding algorithm is employed to further improve MT-Deluge's performance. Experiments with 20 TelosB motes on three representative topologies show that MT-Deluge shortens the dissemination delay by 37.6%~48.6% compared to a typical data dissemination protocol while keeping the merits of loss resilience.

关键词： Decoding Instruction sets Receivers Encoding Delay Protocols Synchronization

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Using RSS with difference method in localization algorithm for sensor networks

Using RSS with difference method in localization algorithm f...

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International Conference on Information science and Engineering

作者： Weng, Kai Chen, Chun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9781424480968

In this paper we present a scalable range-free algorithm for localization in wireless sensor network. Accurate and distributed localization algorithms are needed for a wide variety of wireless sensor network applications. Our algorithm finds neighbor nodes by comparing RSS and estimates the location from these neighbors' location. We also provide an evaluation of our algorithm on a simulated sensor network. © 2010 IEEE.

关键词： Wireless sensor networks

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A level-set based tracking approach for surveillance video with fusion and occlusion

A level-set based tracking approach for surveillance video w...

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Pacific Rim Symposium on Image and Video Technology

作者： Hong, Chaoqun Li, Na Song, Mingli Bu, Jiajun Chen, Chun Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9780769542850

Traditional level-set-based methods of tracking contours suffered from occlusion and fusion. In this paper, the proposed method introduces dynamic incident detection to find and handle occlusion and fusion. Color histogram of the hue component in HSV color space is used to identify the objects re-entering after occlusion. On the other hand, object features including the size and the motion pattern are utilized to remove the fake regions that are fused with the object region. Besides, a comprehensive foreground extraction (CFE) method based on the combination of background subtraction and Local Binary Pattern (LBP) is proposed. It is fast and robust. Our method is Experiments show that the proposed approach outperforms previous methods on both speed and subjective quality. © 2010 IEEE.

关键词： Local binary pattern

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Music recommendation by unified hypergraph: Combining social media information and music content

Music recommendation by unified hypergraph: Combining social...

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18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Bu, Jiajun Tan, Shulong Chen, Chun Wang, Can Wu, Hao Zhang, Lijun He, Xiaofei Zhejiang Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou 310027 China State Key Laboratory of CAD and CG College of Computer Science Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781605589336

Acoustic-based music recommender systems have received increasing interest in recent years. Due to the semantic gap between low level acoustic features and high level music concepts, many researchers have explored collaborative filtering techniques in music recommender systems. Traditional collaborative filtering music recommendation methods only focus on user rating information. However, there are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as *** and Pandora. This information is valuable for music recommendation. However, there are two challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. In this paper, we propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations. Experiments on a data set collected from the music social community *** have demonstrated the effectiveness of our proposed algorithm. © 2010 ACM.

关键词： Recommender systems

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