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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

引用

7th IFIP International Conference on intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Semi-automatic image annotation using sparse coding

Semi-automatic image annotation using sparse coding

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University Beijing China School of Medicine Boston University Boston MA USA

Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. It has become a new research focus and many techniques have been proposed to solve this problem. In this paper, a novel semi-auto image annotation technique is proposed. The new developed method uses a label transfer mechanism to automatically recommend promising tags to each image by assigning each image a category label first. Since image representation is one of the key problems in image annotation, we utilize a sparse coding based spatial pyramid matching as an effective way to model and interpret image features. Experimental results demoustrate that the proposed method outperforms the current state-of-the-art methods on two benchmark image datasets.

关键词： Abstracts Ice Snow Marine vehicles Filtering Software

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

引用

13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Clustering data and imprecise concepts

Clustering data and imprecise concepts

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University China Robotics Institute CMU USA Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University China

Cluster analysis is the assignment of grouping a set of observations into clusters so that observations in the same cluster are similar in some sense. One of the key features for clustering is how to define a sensible similarity measure. However, classical clustering algorithms have no ability to cluster data instances and imprecise concepts using traditional distance measures. In this paper, we proposed a (dis)similarity measure based on a new knowledge representation framework called label semantics. Based on this new measure, we can automatically cluster data instance and descriptive concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem in image classification demonstrate the effectiveness of the new proposed clustering algorithm. Since the new proposed measure can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to clustering data instance and imprecise concepts represented by other granularities.

关键词： Pragmatics Clustering algorithms Semantics Image color analysis Silicon Algorithm design and analysis Humans

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Image scene categorization using multi-bag-of-features

Image scene categorization using multi-bag-of-features

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory (ICMLL) School of ASEE Beihang University China Robotics Institute Carnegie Mellon University USA

Image scene classification, the classification of images into semantic categories, e.g. city, urban, sea, etc, has recently become a vigorous research focus in computer vision for its broad application prospect. In this paper, we propose a novel approach to understand image semantic scene based on multi-bag-of-features. We aim to design an efficient but simple scene classification algorithm via fusing multiple low-level image features. Experimental results demonstrate that the proposed approach offers an effective way to classify the complex image scenes by using a multi-bag-of-features model.

关键词： Support vector machines Books Quantization Image segmentation Wide area networks

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Hybrid moving object detection system based on key frame extraction

Hybrid moving object detection system based on key frame ext...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yu Du Xueyao Wang Shi-Ting Wang Shuangshuang Xue Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University Beijing China School of Automation Science and Electronic Engineering (ASEE) Beihang University Beijing China Robotics Institute Carnegie Mellon University USA

In this paper we construct a hybrid moving object detection system. In this system, we first use the frame difference method to extract key frames in a given video sequence, then use the optical flow method and the HSV background subtraction method to extract the moving objects, respectively. We propose two hybrid methods: Improved Optical Flow method and Improved HSV Background Subtraction method. Experimental results have shown that our proposed system has strong robustness and be effective to detect moving objects in various conditions. We also introduce a human-computer interaction tool for selecting the focus area for users. This system will largely benefit for real-world video surveillance applications.

关键词： Computer vision Lighting Image motion analysis Object detection Optical imaging Accuracy Video sequences

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引用

International Conference on machine learning and Cybernetics

作者： Zhang, Weifeng Qin, Zengchang Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781424465262

The concept of label semantics[6] proposed by Lawry is a random set framework for modeling with words. It proposes two fundamental and inter-related measures of the a appropriateness of labels as descriptions of an object or value. In this paper, based on an introduction of logical expressions based on label semantics , we present a new measure to evaluate the distance between two logical expressions of fuzzy labels. Two fundamental and important properties of this distance are discussed. It is theoretically proved and tested by experiments that the new measure can explain the dissimilarity of linguistic rules well. © 2010 IEEE.

关键词： Semantics

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引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China

The concept of label semantics proposed by Lawry is a random set framework for modeling with words. It proposes two fundamental and inter-related measures of the a appropriateness of labels as descriptions of an object or value. In this paper, based on an introduction of logical expressions based on label semantics, we present a new measure to evaluate the distance between two logical expressions of fuzzy labels. Two fundamental and important properties of this distance are discussed. It is theoretically proved and tested by experiments that the new measure can explain the dissimilarity of linguistic rules well.

关键词： Measurement Educational institutions

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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A HSC-based sample selection method for support vector machine

A HSC-based sample selection method for support vector machi...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Qing He Ning Li Zhong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China Chinese Academy and Sciences China

关键词： Support vector machines Training Classification algorithms machine learning Accuracy Testing machine learning algorithms

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