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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 580 条记录，以下是441-450 订阅

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A New Algorithm of pattern recognition Based on RBF Neural Network and Monkey-King Genetic Algorithm

A New Algorithm of Pattern Recognition Based on RBF Neural N...

引用

1st international workshop on Education Technology and Computer Science

作者： Yin, Ximing Yan, Ying Chengdu Text Coll Basic Dept Chengdu Peoples R China North China Elect Power Univ Dept Econ & Management Baoding Peoples R China

ISBN: (纸本)9780769535579

A new algorithm of pattern recognition based on RBF neural network and Monkey-King genetic algorithm (MK-RBFNN) is prsented. The algorithm includes two parts. The first parts is that Monkey-King genetic algorithm is introduced to determine the initial positions and number of the hidden layer centers in RBF network. The second part is to set up the RBF neural network to do pattern recognition by learning and training the input sample data. Finally, experiments are implemented on datasets as Iris and WINES, which shows that the proposed algorithm has higher recognition ability compared with the conventional methods.

关键词： genetic algorithm Mongkey-King neural network pattern recognition RBF

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Relative Distance-based Laplacian Eigenmaps

Relative Distance-based Laplacian Eigenmaps

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Zhong, Guoqiang Hou, Xinwen Liu, Cheng-Lin Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China

ISBN: (纸本)9781424441990

In many areas of pattern recognition and machine learning, low dimensional data are often embedded in a high dimensional space. There have been many dimensionality reduction and manifold learning methods to discover the low dimensional representation from high dimensional data. Locality based manifold learning methods often rely on a distance metric between neighboring points. In this paper, we propose a new distance metric named relative distance, which is learned from the data and can better reflect the relative density. Combining the relative distance with Laplacian Eigenmaps (LE), we obtain a new algorithm called Relative Distance-based Laplacian Eigenmaps (RDLE) for nonlinear dimensionality reduction. Based on two different definitions of the relative distance, we give two variations of the RDLE. For efficient projection of out-of-sample data, we also present the linear version of RDLE, LRDLE. Experimental results on toy problems and real-world data demonstrate the effectiveness of our methods.

关键词： Nonlinear dimensionality reduction manifold learning Laplacian eigenmaps relative distance

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Neighborhood Balance Embedding for Unsupervised Dimensionality Reduction

Neighborhood Balance Embedding for Unsupervised Dimensionali...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Sun, Mingming Liu, Chuancai Yang, Jingyu Nanjing Univ Sci & Technol Dept Comp Sci Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the correct inner structure of data sets. In this paper, we proposed a new locality-based manifold learning method - Neighborhood Balance Embedding. The proposed method share the same 'neighborhood preserving' property with other manifold learning methods, however, it describe the local structure in a different way, which makes each neighborhood like as rigid balls, thus prevents the overlapping phenomenon which often happens when coping with highly nonlinear data. Experimental results on the data sets with high nonlinearity show good performances of the proposed method.

关键词： Manifold learning Reconstruction Error Unsupervised Kernel Regression Neighborhood Balance Embedding

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A Unified Framework for Kernelization: the Empirical Kernel Feature Space

A Unified Framework for Kernelization: the Empirical Kernel ...

引用

Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Xiong, Huilin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

ISBN: (纸本)9781424441990

In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional empirical feature space, its nonlinear kernel machine is then established in the original input data space. This method is different from the conventional kernel-trick based kernelization, and more importantly, the final nonlinear kernel machines, called empirical kernel machines, are shown to be more efficient in many real-world applications, such as face recognition and facial expression recognition, than the kernel-trick based kernel machines.

关键词： pattern recognition kernelization kernel machines discriminant analysis

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Global Sparse Representation Projections for Feature Extraction and Classification

Global Sparse Representation Projections for Feature Extract...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Lai, Zhihui Jin, Zhong Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

In this paper, we propose a novel supervised learning method called Global Sparse Representation Projections (GSRP) for linear dimensionality reduction. GSRP can be viewed as a combiner of sparse representation and manifold learning. But differing from the recent manifold learning methods such as Local Preserving Projections (LPP), GSRP introduces the global sparse representation information into the objective function. Since sparse representation can implicitly employ the "local" structure of the data by imposing the sparsity prior, we take advantages of this property to characterize the local structure. By combining the local interclass neighborhood relationship and sparse representation information, GSRP aims to preserve the sparse reconstructive relationship of the data and simultaneously maximize the interclass separability. Comprehensive comparison and extensive experiments show that GSRP achieves higher recognition rates than the state-of-the-art techniques such as LPP and Sparsity Preserving Projections (SPP).

关键词： Sparse representation Manifold learning Dimensionality reduction Feature extraction

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Local Discriminant Space Alignment

Local Discriminant Space Alignment

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Wu, Songsong Li, Yongzhi Yang, Jingyu Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Peoples R China Nanjing Univ Sch Informat Sci & Technol Nanjing 210037 Peoples R China

ISBN: (纸本)9781424441990

Dimensionality reduction has been demonstrated to be an effective way for feature extraction in the pattern recognition task. In this paper, a new manifold learning algorithm, Local Discriminant Space Alignment (LDSA), is developed for nonlinear dimensionality reduction. In LDSA, the discriminant structure and the local geometry of data manifold is learned by constructing a local space for each data point through local discriminant analysis, and those discriminant subspaces are aligned to give the internal global coordinates of data points with respect to the underlying manifold. To solve the out of sample problem, the linearization of LDSA (LLDSA) is also proposed and is applied to face recognition. The Experimental results on ORL and Yale database showed the effectiveness of LLDSA in comparison with existing dimensionality reduction algorithms designed for feature extraction.

关键词： Dimensionality Reduction Local Discriminant Space Alignment Manifold learning Face recognition

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Application of PrefixSpan* Algorithm in Malware Detection Expert System

Application of PrefixSpan* Algorithm in Malware Detection Ex...

引用

1st international workshop on Education Technology and Computer Science

作者： Wang, Lina Tan, Xiaobin Pan, Jianfeng Xi, Hongsheng Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9780769535579

Malware detection is an important application of data mining. Most of the previously developed sequential pattern mining methods are Apriori-like, which still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. So we need a novel sequential pattern mining method, called PrefixSpan*, which uses brief projection database in stead of projection database in PrefixSpan. In this paper, we propose a behavior-based detection system, which combines data mining and expert system technique to detect malware in our hosts. The PrefixSpan* algorithm mines association rules in the malware behavior sequence database to form malware behavior pattern database;the expert system matches facts and rules and gives the final result. To verify the correctness and effectiveness of our algorithm, we test and analyze some samples in the experiment section.

关键词： malware defection sequential pattern mining PrefixSpan* Algorithm Projected database

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Histogram-Based Fisher Information Embedding for Manifolds Clustering and Visualization

Histogram-Based Fisher Information Embedding for Manifolds C...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Zou, Jian Liu, ChuanCai Zhang, Yue Nanjing Univ Sci & Technol Dept Comp Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

In this paper, a nonparametric histogram-based fisher information embedding method is presented for clustering and visualizing data sets with non-Euclidean geometric structures. It is on the assumption that each data set is derived from a probability distribution that can be characterized by a probability density function (PDF) lying on a statistical manifold. The dissimilarities between data sets can be quantified with the corresponding geodesic distances on statistical manifold from the views of information geometry. Our method is designed to convert data distribution information into simplex discretely, thereby a similarity measure can be adopted with simplex geometry, further clustering or visualizing original manifolds can be performed on a low-dimensional Euclidean subspace via manifold learning. Experiments on clustering Swiss Roll and s-curve submanifolds and reconstructing a sphere demonstrate the effectiveness of our method.

关键词： Information geometry statistical manifold information embedding manifold learning clustering visualization

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Decision Support System (DSS) - Form, Development and Future

Decision Support System (DSS) - Form, Development and Future

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1st international workshop on Education Technology and Computer Science

作者： Fang, Bin Tianjin Univ Sch Management Tianjin 300072 Peoples R China

The Decision Support System (DSS) applies various data and models to Human-machine Interface (HMI) to assist decision-makers at each level in achieving scientific decisions. The DSS was originated in 1970s, but has se... 详细信息

ISBN: (纸本)9780769535579

关键词： DSS data Warehouse OLAP data mining

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Local Marginal Projection and Its Applications

Local Marginal Projection and Its Applications

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Mao, Hong-ben Li, Yong-zhi Wu, Song-song Liu, Fen-xiang Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

ISBN: (纸本)9781424441990

Based on UDP and MFA, we propose a new un-supervised feature extraction algorithm, LMP (Local Marginal Projection), which is built on local quality. It measures the non-local quantities by the nearest sample between two locals. The goal of LMP is to find a projection that can maximize the distance of the sample in the same local and in different locals, in which case, the data can be projected into low-dimension easily. Besides, this projection could deal with the nonlinear and high-dimensional problem. The experiment on ORL and Yale face database shows that LMP algorithm can describe the high-dimensional data and can embed the nonlinear data Swiss-Hole into low-dimension space with a reasonable visual effectively.

关键词： Manifold learning face recognition low-dimensional embed feature extraction

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