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International Joint Conference on Neural Networks (IJCNN)

作者： Shan-Wen Zhang Ying-Ke Lei De-Shuang Huang Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy and Sciences Hefei Anhui China Department of Automation University of Science and Technology Hefei China Department of Information Electronic Engineering Institute Hefei China

Classical linear dimensional reduction algorithms, such as Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) have been widely used in computer vision and pattern recognition. However, when dealing with the multidimensional dataset, they usually first transform the original data to vectors, and then analyze the data in such a high dimensional space. This process inevitably results in some obvious disadvantages. This paper proposes a novel two-dimensional dimensionality reduction algorithm called 2D Neighborhood Discriminant Projection (2D-NDP), which is based directly on 2D image matrices rather than 1D vectors. 2D-NDP detects the intrinsic class-relationships between the images by incorporating both class label information and neighborhood information. It can optimally preserve not only the local class information but discriminant information as well. Under the orthogonal constrain, 2D-NDP is developed as orthogonal 2D-NDP for classification. Experiments on the face database and the plant leaf database demonstrate that orthogonal 2D-NDP is effective and feasible for classification.

关键词： Artificial neural networks Databases

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A noise handling method for hyper surface classification

A noise handling method for hyper surface classification

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Tingting Li Fuzhen Zhuang Qing He Chinese Academy and Sciences China Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China

Hyper surface classification (HSC) based on Jordan Curve Theorem is proven to be a simple and effective method to classify large datasets. Like most of classification algorithms, noise could also impact its accuracy even if the HSC algorithm limits the influence of noise in a local small region. In this paper, we propose a method that intuitively captures the primary goal of improving the accuracy of HSC when trained on noisy training datasets. The proposed method uses a separate pruning set to test whether the hyper surfaces covering few samples are assigned wrong labels due to the existence of noise. And then reassigns them appropriate labels if necessary. We compare the performance of HSC with and without the noise handling method. The promising experimental results indicate that the noise handling method can improve the accuracy of HSC when trained on noisy datasets, while keeping good performance when it is applied to datasets without noise. At the same time, it also reduces the model complexity of HSC to some extent.

关键词： Noise Accuracy Training Surface treatment Noise measurement Classification algorithms Iris

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Web Service Composition on the Environment Level

Web Service Composition on the Environment Level

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Guangjun Cai The 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

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, creating dynamic business processes and agile applications. Service composition plays a very important role in it. One important problem related to service composition is about what the content of service description is. Most approaches focus on the description about web services themselves, and fail to consider the behaviour of the services in composition. This paper introduces a composition approach on the environment level. The basic idea of it is to develop a model-based approach by using the stability character of environment to encapsulate the changing of the service and the request. Our approach both facilitate to improve the composition efficiency and is more appropriate for compositing complex services or behaviour services based on its’ behaviour. A running travel example is used to illustrate our approach.

关键词： Service-oriented computing environment level service composition service behaviour

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3D mesh segmentation based on boundary strength

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Ruan Jian Xue Bao/Journal of Software 2010年第SUPPL. 1期21卷 183-193页

作者： Sun, Xiao-Peng Ji, Yan-Jie Wei, Xiao-Peng Department of Computer and Information Technology Liaoning Normal University Dalian 116029 China School of Mechanical and Engineering Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

In this paper, we present a novel and fast algorithm for 3D meshes segmentation based on the geometric information of boundary. Firstly, according to the topological relationship of original mesh faces, we construct the dual graph, and set the weight of vertex and edge on the basis of geometric information of mesh faces. Using the k-way multilevel cutting method, we segment the dual graph quickly to obtain the pre-segmentation and initial boundary for each patch. Then we define the boundary strength function and the feature boundary of each patch, to construct a deformation contour on the patch's boundary. By minimizing the energy of the deformation contour, the initial boundary is driven to approach the feature boundary. Finally, the original mesh is segmented into several meaningful patches in accordance with the minimal rule. The experiment results suggest that our algorithm is efficient and effective, and is applicable to a variety of triangular mesh models with prominent shape features. © by Institute of Software, the Chinese Academy of Sciences.

关键词： Deformation

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A Laplacian eigenmaps based semantic similarity measure between words

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作者： Wu, Yuming Cao, Cungen Wang, Shi Wang, Dongsheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing 100-190 China Graduate University of Chinese Academy of Sciences No. 19 Yu Quan Road Shi Jing Shan Distinct Beijing 100-049 China

ISBN: (纸本)3642163262

The measurement of semantic similarity between words is very important in many applicaitons. In this paper, we propose a method based on Laplacian eigenmaps to measure semantic similarity between words. First, we attach semantic features to each word. Second, a similarity matrix ,which semantic features are encoded into, is calculated in the original high-dimensional space. Finally, with the aid of Laplacian eigenmaps, we recalculate the similarities in the target low-dimensional space. The experiment on the Miller-Charles benchmark shows that the similarity measurement in the low-dimensional space achieves a correlation coefficient of 0.812, in contrast with the correlation coefficient of 0.683 calculated in the high-dimensional space, implying a significant improvement of 18.9%. © 2010 IFIP.

关键词： Semantics

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D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification

D-LDA: A Topic Modeling Approach without Constraint Generati...

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IEEE International Conference on Data Mining (ICDM)

作者： Fuzhen Zhuang Ping Luo Zhiyong Shen Qing He Yuhong Xiong Zhongzhi Shi Chinese Academy and Sciences China Hewlett Packard Laboratories China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Innovation Works USA

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pair wise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods.

关键词： Data models Mathematical model Clustering algorithms Analytical models Companies Equations Web pages

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A Real-Time Categorization and Clustering Method for Text Data of Laws and Regulations

A Real-Time Categorization and Clustering Method for Text Da...

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International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Bianping Su Rong Wang Yiping Wang School of Science Xi''an University of Architecture and Technology Xi'an China Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Science Chang'an University Xi'an China

Taking the features of data in low and high frequency texts and the frequencies which such features emerge in a single text into consideration, the paper sets up a vector space model for part of texts of field. Then the paper also establishes a classifying and clustering method with features of classification and clustering by designing and constructing the two-dimensional analytic indexes of similarities and differences between field texts. This method is designed for field texts because it is quite suitable for effective machine learning and can extend the text data and textual categories dynamically in real-time. Meantime, it solves the single-label classification and multi-label classification issues at one time, overcoming the defects of previous text classifying methods which can only expand data instead of capacity. The general text clustering methods have many defects: they are not suitable for high-dimensional data sets or large data sets; they don't have the text data and category expanding function and they can not handle the outlier data problem well. On the contrary, this new method can offer solutions to these defects. According to this method, the corresponding algorithm has been established and the effectiveness of the method has been proven by the experiment on the data sets of laws and regulations of construction industry in Shaanxi Province in China.

关键词： Support vector machine classification Clustering methods Real time systems Clustering algorithms Text categorization Feature extraction

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Predicting RNA secondary structure with Tabu Search

Predicting RNA secondary structure with Tabu Search

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IEEE International Conference on Cognitive Informatics

作者： Yongguo Liu Jianrui Hao Jun Peng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China College of Electronic Information Engineering Chongqing University of Science and Technology Chongqing China

In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective.

关键词： RNA Computer simulation Space exploration Neodymium Nickel Laboratories

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Combining the Missing Link: An Incremental Topic Model of Document Content and Hyperlink

Combining the Missing Link: An Incremental Topic Model of Do...

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International Asia-Pacific Web Conference (APWEB)

作者： Huifang Ma Zhixin Li Zhongzhi Shi Chinese Academy and Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction.

关键词： Laboratories intelligent structures information processing Computers Web pages Programmable control Adaptive control Learning systems Predictive models Data mining

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Gray-scale super-resolution for face recognition from low Gray-scale resolution face images

Gray-scale super-resolution for face recognition from low Gr...

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IEEE International Conference on Image Processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Today's camera sensors usually have a high gray-scale resolution, e.g. 256, however, due to the dramatic lighting variations, the gray-scales distributed to the face region might be far less than 256. Therefore, besides low spatial resolution, a practical face recognition system must also handle degraded face images of low gray-scale resolution (LGR). In the last decade, low spatial resolution problem has been studied prevalently, but LGR problem was rarely studied. Aiming at robust face recognition, this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly. Possible solutions to the problem are discussed and grouped into three categories: gray-scale resolution invariant features, gray-scale degradation modeling and Gray-scale Super-Resolution (GSR). Then, we propose a Coupled Subspace Analysis (CSA) based GSR method to recover the high gray-scale resolution image from a single input LGR image. Extensive experiments on FERET and CMU-PIE face databases show that the proposed method can not only dramatically increase the gray-scale resolution and visualization quality, but also impressively improve the accuracy of face recognition.

关键词： Image processing Conferences

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