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Accelerated algorithm to incomplete nonnegative matrix factorization

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2011年第2期39卷 291-295页

作者： Shi, Jia-Rong Jiao, Li-Cheng Shang, Fan-Hua Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi 710071 China

Nonnegative matrix factorization (NMF) is an increasingly popular technique for data processing and analysis. For an incomplete data matrix, the weighted nonnegative matrix factorization (WNMF) is employed to decompose it. But the searching step size in WNMF is not optimal along the given searching direction. This paper studies the incomplete nonnegative matrix factorization (INMF) and proposes an accelerated algorithm. First, INMF is transformed into solving alternatively two nonnegative least squares (NNLS) problems. For each NNLS problem, the exact step size is chosen along the searching direction. Then, the complexity of NNLS problems is analyzed. Finally, experimental results show that the proposed method outperforms WNMF.

关键词： Matrix algebra

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Active learning for sparse least squares support vector machines

Active learning for sparse least squares support vector mach...

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3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011

作者： Zou, Junjie Yu, Zhengtao Zong, Huanyun Zhao, Xing School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology Kunming 650051 China

ISBN: (纸本)9783642238864

For least squares support vector machine (LSSVM) the lack of sparse, while the standard sparse algorithm exist a problem that it need to mark all of training data. We propose an active learning algorithm based on LSSVM to solve sparse problem. This method first construct a minimum classification LSSVM, and then calculate the uncertainty of the sample, select the closest category to mark the sample surface, and finally joined the training set of labeled samples and the establishment of a new classifier, repeat the process until the model accuracy to meet Requirements. 6 provided in the UCI data sets on the experimental results show that the proposed method can effectively improve the sparsity of LSSVM, and can reduce the cost labeled samples. © 2011 Springer-Verlag.

关键词： Classification (of information)

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On modeling MapReduce with granular computing

On modeling MapReduce with granular computing

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zhang, Bo Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology China University of Mining and Technology Xuzhou China

ISBN: (纸本)9781457703713

Cloud computing focuses on supporting high scalable and high available parallel and distributed computing, based on the infrastructure built on top of large scale clusters which contain a large number of cheap PC servers, to process the huge amounts of data generated by Internet. As the core of cloud computing, Google's MapReduce programming model and Google File System (GFS) provide such computing power. Granular computing (GrC) is an objective world outlook and methodology. From the point of GrC, this paper surveys the Google's MapReduce programming model, analyses how to express its process of data granulation and computing more accurately and more strictly during the parallel and distributed computing. © 2011 IEEE.

关键词： MapReduce

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Decoding optimization for Chinese-English machine translation via a dependent syntax language model

Decoding optimization for Chinese-English machine translatio...

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3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011

作者： Liu, Ying Yu, Zhengtao Zhang, Tao Zhao, Xing School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology Kunming 650051 China

ISBN: (纸本)9783642238956

Decoding is a core process of the statistical machine translation, and determines the final results of it. In this paper, a decoding optimization for Chinese-English SMT with a dependent syntax language model was proposed, in order to improve the performance of the decoder in Chinese-English statistical machine translation. The data set was firstly trained in a dependent language model, and then calculated scores of NBEST list from decoding with the model. According to adding the original score of NBEST list from the decoder, the NBEST list of machine translation was reordered. The experimental results show that this approach can optimize the decoder results, and to some extent, improve the translation quality of the machine translation system. © 2011 Springer-Verlag.

关键词： Machine translation

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Adjoining tree-to-string translation

Adjoining tree-to-string translation

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49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011

作者： Liu, Yang Liu, Qun Lü, Yajuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781932432879

We introduce synchronous tree adjoining grammars (TAG) into tree-to-string translation, which converts a source tree to a target string. Without reconstructing TAG derivations explicitly, our rule extraction algorithm directly learns tree-to-string rules from aligned Treebank-style trees. As tree-to-string translation casts decoding as a tree parsing problem rather than parsing, the decoder still runs fast when adjoining is included. Less than 2 times slower, the adjoining tree-tostring system improves translation quality by +0.7 BLEU over the baseline system only allowing for tree substitution on NIST Chinese- English test sets. © 2011 Association for Computational Linguistics.

关键词： Trees (mathematics)

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Local regression model for automatic face sketch generation

Local regression model for automatic face sketch generation

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International Conference on Image and Graphics

作者： Ji, Naye Chai, Xiujuan Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769545417

As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo;(2) our synthesized sketches presents more pencil sketch texture. © 2011 IEEE.

关键词： Regression analysis

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MUSIZ: A generic framework for music resizing with stretching and cropping 11

MUSIZ: A generic framework for music resizing with stretchin...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhang, Liu Wang, Chaokun Ba, Yiyuan Wang, Hao Wang, Jianmin Department of Computer Science and Technology Tsinghua University China School of Software Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology China Key Laboratory for Information System Security Ministry of Education China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China

ISBN: (纸本)9781450306164

Content-aware music adaption, i.e. music resizing, in temporal constraints starts drawing attention from multimedia communities because of the need of real-world scenarios, e.g. animation production and radio advertisement production. The goal of music resizing is to change the length of a music track to a user preferred length using a series of basic operations, e.g. compression, prolonging, cropping and repeating. The only existing music resizing approach so far, called LyDAR, suffers from some limitations. For example, it cannot support prolonging a music track and cannot compress music pieces with very small stretch rates. In this paper, we propose MUSIZ, a generic framework for MUsic reSIZing. Observing the diversity of quality degradation for different segments, we propose the concept of stretch-resistance to measure the degree of quality degradation after a segment is stretched. MUSIZ stretches high stretch-resistance segments intensively and relieves low stretch-resistance segments to reduce the negative impact on the stretched music piece. For short length resizing requests, we develop a contiguity-preservative cropping algorithm to remove segments before stretching, while smoothing the abrupt change at the joint between two segments. Comprehensive experimental results show that MUSIZ is superior to the existing approaches. © 2011 ACM.

关键词： stretch-resistance music retargeting music resizing

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A general framework for rule acquisition based on tolerance granular computing in IIDSs

A general framework for rule acquisition based on tolerance ...

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Zuqiang, Meng Zhongzhi, Shi Ke, Xu College of Computer Electronics and Information Guangxi University Nanning 530004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781457703713

This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs;secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree;finally, an example is given to show the efficiency of our framework. © 2011 IEEE.

关键词： Granular computing

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Nondominated individual selection strategy based on adaptive partition for evolutionary multi-objective optimization

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第4期48卷 545-557页

作者： Gong, Maoguo Cheng, Gang Jiao, Licheng Liu, Chao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Many real world problems involve the simultaneous optimization of various and often conflicting objectives. These optimization problems are known as multi-objective optimization problems. Evolutionary multi-objective optimization, whose main task is to deal with multi-objective optimization problems by evolutionary computation techniques, has become a hot topic in evolutionary computation community. The solution diversity of multi-objective optimization problems mainly focuses on two aspects, breadth and uniformity. After analyzing the traditional methods which were used to maintain the diversity of individual in multi-objective evolutionary algorithms, a novel nondominated individual selection strategy based on adaptive partition is proposed. The new strategy partitions the current trade-off front adaptively according to the individual's similarity. Then one representative individual will be selected in each partitioned regions for pruning nondominated individuals. For maintaining the diversity of the solutions, the adaptive partition selection strategy can be incorporated in multi-objective evolutionary algorithms without the need of any parameter setting, and can be applied in either the parameter or objective domain depending on the nature of the problem involved. In order to evaluate the validity of the new strategy, we apply it into two state-of-the-art multi-objective evolutionary algorithms. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.

关键词： Multiobjective optimization

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Parallel outlier detection using KD-tree based on MapReduce

Parallel outlier detection using KD-tree based on MapReduce

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2011 3rd IEEE International Conference on Cloud Computing technology and Science, CloudCom 2011

作者： He, Qing Ma, Yunlong Wang, Qun Zhuang, Fuzhen Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769546223

Distributed and Parallel algorithms have attracted a vast amount of interest and research in recent decades, to handle large-scale data set in real-world applications. In this paper, we focus on a parallel implementation of KD-Tree based outlier detection method to deal with large-scale data set. As one of the state-of-the-art outlier detection methods, KD-Tree based has been approved to be an effective algorithm. However, it still cannot process large-scale data set efficiently due to its serial implementation. Based on the current and powerful parallel programming framework - MapReduce, we propose to implement the parallel KD-Tree based outlier detection algorithm (e.g., PKDTree for short). Experimental results demonstrate the efficiency of PKDTree according to the evaluation criterions of scaleup, speedup and sizeup. © 2011 IEEE.

关键词： MapReduce

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