检索结果-南通市图书馆

dimensionality reduction with adaptive graph

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2013年第5期7卷 745-753页

作者： Lishan QIAO Limei ZHANG Songcan CHEN Department of Mathematics Science Liaocheng University Liaocheng 252000 China Department of Computer Science and Engineering Nanjing University of Aeronautics & Astronautics Nanjing 210016 China

Graph-based dimensionality reduction （DR） methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is inde- pendent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an al- gorithm called dimensionality reduction with adaptive graph （DRAG）, whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a pre- specified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update for- mula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.

关键词： dimensionality reduction graph construction face recognition

dimensionality reduction of hyperspectral images of vegetation and crops based on self-organized maps

在线全文

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2021年第2期8卷 310-327页

作者： David Ruiz Hidalgo Bladimir Bacca Cortés Eduardo Caicedo Bravo Perception and Intelligent Systems Research Group School of Electrical and Electronic EngineeringUniversidad del ValleSantiago de Cali 76001Colombia

Hyperspectral images are multidimensional massive sets of information that have shown a great potential for different kind of applications as urban mapping,environmental management,vegetation and crops supervision and mineral ***,due to its high dimensional nature and the high variability of the spectral information,the dimensionality reduction process is one of the main challenges in processing hyperspectral *** aim of dimensionality reduction is to eliminate redundant information and simplify the subsequent processes of classification and the search of *** this context,several dimensionality reduction methods have been proposed,but most of them are not flexible enough to deal with the particular features of the hyperspectral *** this way,the use of intelligent methods as neural networks and specially an unsupervised approach as self-organized maps,may improve the dimensionality reduction stage and the final classification *** paper proposes an unsupervised method for the dimensionality reduction of hyperspectral images based on Kohonen self-organized maps,which,compared with other traditional methods such as principal component analysis(PCA)and wavelet decomposition,provides better classification *** results provided in this paper use an RBF(radial basis function)*** average,the proposed method provides a 64%dimensionality reduction and an 88.5%classification *** results suggest that the dimensionality reduction algorithm based on self-organized maps is an efficient approach compared with other popular *** is due to the ability of self-organized maps to automatically detect(self-organizing)relationships within the set of input patterns,which provides flexibility to deal with the special features of the hyperspectral images.

关键词： dimensionality reduction Hyperspectral images Principal component analysis Radial basis function Remote sensing Self-organized maps Wavelet transform

dimensionality reduction by Mutual Information for Text Classification

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Beijing Institute of Technology 2005年第1期14卷 32-36页

作者：刘丽珍宋瀚涛陆玉昌 Information Engineering College Capital Normal University Beijing100037 China School of Information Science and Technology Beijing Institute of Technology Beijing 100081 China Laboratory

The frame of text classification system was presented. The high dimensionality in feature space for text classification was studied. The mutual information is a widely used information theoretic measure, in a descriptive way, to measure the stochastic dependency of discrete random variables. The measure method was used as a criterion to reduce high dimensionality of feature vectors in text classification on Web. Feature selections or conversions were performed by using maximum mutual information including linear and non-linear feature conversions. Entropy was used and extended to find right features commendably in pattern recognition systems. Favorable foundation would be established for text classification mining.

关键词： text classification mutual information dimensionality reduction

dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

在线全文

学校读者我要写书评

暂无评论

Journal of Data and Information Science 2023年第4期8卷 102-124页

作者： Wei Cui Zhiqiang Xu Ren Mu School of Statistics Jilin University of Finance and EconomicsChangchunJilin 130117China

Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical ***,the interpretability of these models presents a persistent ***/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class *** contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and ***:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life *** findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring *** also maintains compatibility with other ***,DRMBIP-v finds the optimal result,showing exceptional *** of the results reveals that all classes have a high *** limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction *** the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or *** necessitates an artificial adjustment to the overflow component within the objective *** implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional *** by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data ***/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filt

关键词： Integer programming Multidimensional data dimensionality reduction Life expectancy

dimensionality reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第11期47卷 2481-2496页

作者： S.Srinivasan K.Rajakumar School of Computer Science and Engineering VIT UniversityVellore632014India

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature *** high correlation between these features and the noises greatly affects the classification *** overcome this,dimensionality reduction techniques are widely *** image processing applications recently propose numerous deep learning ***,in hyperspectral image classification,the features of deep learning models are less ***,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research *** handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization *** network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification *** datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental *** dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM *** confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

关键词： Hyperspectral image dimensionality reduction depth-wise separable model water strider optimization self-organized map

dimensionality reduction FOR HYPERSPECTRAL IMAGERY BASED ON FASTICA

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Electronics(China) 2009年第6期26卷 831-835页

作者： Xin Qin Nian Yongjian Li Xiu Wan Jianwei Su Linghua College of Electronic Science and Engineering National University of Defense Technology Changsha 410073 China Simulation Training Center Army Aviation Institute Beijing 101123 China Dalian Communication Sergeant School of Air Force Dalian 116600 China

The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. The virtual dimensionality is introduced to determine the number of dimensions needed to be preserved. Since there is no prioritization among independent components generated by the FastICA,the mixing matrix of FastICA is initialized by endmembers,which were extracted by using unsupervised maximum distance method. Minimum Noise Fraction (MNF) is used for preprocessing of original data,which can reduce the computational complexity of FastICA significantly. Finally,FastICA is performed on the selected principal components acquired by MNF to generate the expected independent components in accordance with the order of endmembers. Experimental results demonstrate that the proposed method outperforms second-order statistics-based transforms such as principle components analysis.

关键词： Hyperspectral imagery dimensionality reduction Independent Component Analysis(ICA)

Multi-label dimensionality reduction and classification with extreme learning machines

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2014年第3期25卷 502-513页

作者： Lin Feng Jing Wang Shenglan Liu Yao Xiao Faculty of Electronic Information and Electrical Engineering School of Computer Science and TechnologyDalian University of Technology School of Innovation Experiment Dalian University of Technology

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis （MLKDA）, is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis （LDA）. In the classification process of multi-label data, the extreme learning machine （ELM） is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization （MDDM） and multi-label linear discriminant analysis （MLDA） in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.

关键词： multi-label dimensionality reduction kernel trick classification.

Unsupervised, Supervised and Semi-supervised dimensionality reduction by Low-Rank Regression Analysis

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2021年第4期30卷 603-610页

作者： TANG Kewei ZHANG Jun ZHANG Changsheng WANG Lijun ZHAI Yun JIANG Wei School of Mathematics Liaoning Normal University College of Computer Science and Artificial Intelligence Wenzhou University Research Center for Information Science Theory and Methodology Institute of Scientific and Technical Information of China E-Government Research Center Chinese Academy of Governance

Techniques for dimensionality reduction have attracted much attention in computer vision and pattern recognition. However, for the supervised or unsupervised case, the methods combining regression analysis and spectral graph analysis do not consider the global structure of the subspace; For semi-supervised case,how to use the unlabeled samples more effectively is still an open problem. In this paper, we propose the methods by Low-rank regression analysis(LRRA) to deal with these problems. For supervised or unsupervised dimensionality reduction, combining spectral graph analysis and LRRA can make a global constraint on the subspace. For semi-supervised dimensionality reduction, the proposed method incorporating LRRA can exploit the unlabeled samples more effectively. The experimental results show the effectiveness of our methods.

关键词： dimensionality reduction Low-rank Supervised Unsupervised Semi-supervised

Multi-view dimensionality reduction via canonical random correlation analysis

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Computer Science 2016年第5期10卷 856-869页

作者： Yanyan ZHANG Jianchun ZHANG Zhisong PAN Daoqiang ZHANG College of Command Information Systems PLA University of Science and Technology Nanjing 210007 China Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

Canonical correlation analysis （CCA） is one of the most well-known methods to extract features from multi- view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross- view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis （RCA）. In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA （KRCA） is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.

关键词： canonical correlation analysis discriminant,multi-view dimensionality reduction