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Obtaining Profiles Based on Localized Non-negative Matrix Factorization 被引量:2
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作者 JIANGJi-xiang XUBao-wen +1 位作者 LUJian-jiang ZhouXiao-yu 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第5期580-584,共5页
Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of informatio... Nonnegative matrix factorization (NMF) is a method to get parts-based features of information and form the typical profiles. But the basis vectors NMF gets are not orthogonal so that parts-based features of information are usually redundancy. In this paper, we propose two different approaches based on localized non-negative matrix factorization (LNMF) to obtain the typical user session profiles and typical semantic profiles of junk mails. The LNMF get basis vectors as orthogonal as possible so that it can get accurate profiles. The experiments show that the approach based on LNMF can obtain better profiles than the approach based on NMF. Key words localized non-negative matrix factorization - profile - log mining - mail filtering CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000), National Research Foundation for the Doctoral Program of Higher Education of China (20020286004).Biography: Jiang Ji-xiang (1980-), male, Master candidate, research direction: data mining, knowledge representation on the Web. 展开更多
关键词 localized non-negative matrix factorization PROFILE log mining mail filtering
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Nonnegative matrix factorization with Log Gabor wavelets for image representation and classification
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作者 Zheng Zhonglong Yang Jie 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第4期738-745,共8页
Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially loc... Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization (NMF) is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis (PCA) and locally linear embedding (LIE) proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied. 展开更多
关键词 non-negative matrix factorization (NMF) Log Gabor wavelets principal component analysis locally linearembedding (LLE)
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Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization 被引量:1
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作者 Xu Liu Tiao-Tiao Liu +3 位作者 Wen-Wen Bai Hu Yi Shuang-Yan Li Xin Tian 《Neuroscience Bulletin》 SCIE CAS CSCD 2013年第3期279-286,共8页
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials (LFPs) based on sparse non negative matrix factor... Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials (LFPs) based on sparse non negative matrix factorization (SNMF). SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory. 展开更多
关键词 sparse non-negative matrix factorization multi-channel local field potentials working memory prefrontal cortex
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稀疏LNMF算法在图像局部特征提取中的应用 被引量:2
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作者 尚丽 苏品刚 +1 位作者 周昌雄 杜吉祥 《计算机工程与应用》 CSCD 北大核心 2011年第30期206-209,233,共5页
考虑自然图像的先验稀疏结构及其特征子空间的局部性,在局部非负矩阵分解(LNMF)算法的基础上,提出一种具有稀疏约束的局部非负矩阵分解(SC-LNMF)神经网络算法。使用两类自然属性不同的图像在不同的维数下对SC-LNMF网络进行训练,该方法... 考虑自然图像的先验稀疏结构及其特征子空间的局部性,在局部非负矩阵分解(LNMF)算法的基础上,提出一种具有稀疏约束的局部非负矩阵分解(SC-LNMF)神经网络算法。使用两类自然属性不同的图像在不同的维数下对SC-LNMF网络进行训练,该方法都能成功地提取出训练图像的局部特征。与NMF、LNMF特征提取方法相比,实验对比结果证明了SC-LNMF算法能够模拟大脑初级视觉系统V1区感受野的特性,进一步证实了该算法在图像局部特征提取中的有效性和实用性。 展开更多
关键词 稀疏约束 局部非负矩阵分解(lnmf) 自然图像 特征提取
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基于EMD-WVD与LNMF的内燃机故障诊断 被引量:18
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作者 牟伟杰 石林锁 +2 位作者 蔡艳平 刘浩 金广智 《振动与冲击》 EI CSCD 北大核心 2016年第23期191-196,202,共7页
内燃机的振动信号是复杂非平稳信号,准确提取内燃机振动信号中的特征信息进行模式识别,是对振动信号进行故障诊断的关键。基于经验模态分解的维格纳时频分析方法,不但保留了维格纳分布的所有优良特,而且还避免了交叉项的干扰,能够有效... 内燃机的振动信号是复杂非平稳信号,准确提取内燃机振动信号中的特征信息进行模式识别,是对振动信号进行故障诊断的关键。基于经验模态分解的维格纳时频分析方法,不但保留了维格纳分布的所有优良特,而且还避免了交叉项的干扰,能够有效地提取内燃机振动信号的特征信息;在此基础之上,针对传统非负矩阵分解非正交的基矩阵导致数据冗余性较大、影响后续故障分类准确率提高的问题,提出采用局部非负矩阵分解的方法,直接对EMD-WVD时频图像的矩阵进行分解,计算用于内燃机故障诊断的特征参数,并利用特征参数进行故障分类。对内燃机4种不同工况的振动信号进行实验,证明基于EMD-WVD与局部非负矩阵分解的方法对内燃机气门间隙的故障诊断的有效性。 展开更多
关键词 内燃机 故障诊断 时频分布 特征提取 局部非负矩阵分解
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基于KVMD-PWVD与LNMF的内燃机振动谱图像识别诊断方法 被引量:17
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作者 牟伟杰 石林锁 +2 位作者 蔡艳平 孙钢 郑勇 《振动与冲击》 EI CSCD 北大核心 2017年第2期45-51,94,共8页
为了直接对内燃机振动谱图像进行诊断识别,提出一种基于改进变分模态分解(VMD)、伪魏格纳时频分析(PWVD)与局部非负矩阵分解(LNMF)的内燃机振动谱图像识别诊断方法。该方法首先针对VMD分解过程中的层数选取问题,提出了一种中心频率筛选... 为了直接对内燃机振动谱图像进行诊断识别,提出一种基于改进变分模态分解(VMD)、伪魏格纳时频分析(PWVD)与局部非负矩阵分解(LNMF)的内燃机振动谱图像识别诊断方法。该方法首先针对VMD分解过程中的层数选取问题,提出了一种中心频率筛选的VMD分解层数改进方法(KVMD),然后将内燃机振动信号利用KVMD分解成一组单分量模态信号,并对生成的各个单分量信号进行伪魏格纳分析处理后表征成振动谱图像;在此基础上,对生成的内燃机KVMD-PWVD振动谱图像分别采用非负矩阵分解(NMF)和LNMF形成编码矩阵,并采用最近邻分类器、朴素贝叶斯分类器和支持向量机对上述编码矩阵直接进行模式识别,以实现内燃机振动谱图像的自动诊断。最后,将该方法应用在内燃机故障诊断实例中,结果表明:该方法改进了传统图像模式识别中的特征参数方法,能有效诊断出内燃机气门间隙故障,三种分类器识别精度均大于93%,其中支持向量机的分类精度最高,达到99.8%,且采用LNMF形成的编码矩阵识别精度整体高于NMF,为内燃机振动诊断探索了一条新途径。 展开更多
关键词 内燃机 故障诊断 时频分布 特征提取 局部非负矩阵
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二维局部非负矩阵分解的路网态势算法
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作者 许榕 吴聪 +1 位作者 蒋士正 陈启美 《上海交通大学学报》 EI CAS CSCD 北大核心 2015年第8期1131-1136,1143,共7页
针对路网态势评测算法存在限于断面、依赖单一指标等的不足,在解析测量指标和测量断面的相关性及局部非负矩阵分解(LNMF)算法的基础上,提出了二维局部非负矩阵分解2DLNMF算法,通过选择合适参数对路网数据进行降维处理,提取路网特征数据... 针对路网态势评测算法存在限于断面、依赖单一指标等的不足,在解析测量指标和测量断面的相关性及局部非负矩阵分解(LNMF)算法的基础上,提出了二维局部非负矩阵分解2DLNMF算法,通过选择合适参数对路网数据进行降维处理,提取路网特征数据,从而实现路网态势评测.仿真结果表明,使用2D-LNMF算法路网态势评测结果更加准确,而在线评测准确性达到95.69%. 展开更多
关键词 路网态势 聚类 二维局部非负矩阵分解 特征提取
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局部子空间映射在人耳识别中的应用
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作者 冷加福 穆志纯 万捷 《计算机工程与应用》 CSCD 北大核心 2009年第8期175-177,共3页
比较了三种用于人耳识别的局部表征方法的识别准确率。通过在USTB2人耳库上的实验,对所采用的空间几何距离及选择的特征数进行了一个系统的比较分析。结果表明,方法的识别性能随所用的空间几何距离公式发生较大变化。
关键词 人耳识别 局部非负矩阵分解 独立分量分析 非负矩阵稀疏分解
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基于小波包时频相平面图及局部非负矩阵分解的内燃机故障诊断研究 被引量:1
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作者 李永香 牟伟杰 +3 位作者 董峰 周国庆 雷鹏 李虎雨 《移动电源与车辆》 2016年第2期41-46,共6页
为实现内燃机气门间隙的自动识别,提出了一种基于小波包时频相平面图及局部非负矩阵分解的内燃机故障诊断方法。该方法首先采用小波包生成内燃机振动谱时频相平面图,然后针对传统非负矩阵分解非正交的基矩阵导致数据冗余性较大、不利于... 为实现内燃机气门间隙的自动识别,提出了一种基于小波包时频相平面图及局部非负矩阵分解的内燃机故障诊断方法。该方法首先采用小波包生成内燃机振动谱时频相平面图,然后针对传统非负矩阵分解非正交的基矩阵导致数据冗余性较大、不利于后续识别率提高的问题,提出采用局部非负矩阵分解的方法对生成的图形进行局部非负矩阵分解,提取出图像的代数特征值并进行故障诊断。对内燃机4种状态下信号的小波包时频相平面图的特征提取和故障分类结果表明,基于小波包时频相平面图与局部非负矩阵分解的方法对内燃机气门间隙的故障诊断的有效性。 展开更多
关键词 内燃机 故障诊断 小波包时频相平面图 特征提取 局部非负矩阵分解
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Single Image Super-Resolution Method via Refined Local Learning
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作者 唐松泽 肖亮 刘鹏飞 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期26-31,共6页
In this paper,we propose a refined local learning scheme to reconstruct a high resolution(HR)face image from a low resolution(LR)observation.The contribution of this work is twofold.Firstly,multi-direction gradient fe... In this paper,we propose a refined local learning scheme to reconstruct a high resolution(HR)face image from a low resolution(LR)observation.The contribution of this work is twofold.Firstly,multi-direction gradient features are extracted to search the nearest neighbors for each image patch,then the non-negative matrix factorization(NMF)is used to reduce the complexity in weight calculation,and the initial HR embedding is estimated from the training pairs by preserving local geometry.Secondly,a global reconstruction constraint and post-processing by non-local filtering is incorporated into super-resolution(SR)reconstruction process to reduce the image artifacts and further improve the image visual quality.Experimental results show that the proposed algorithm improves the SR performance both in subjective and objective assessments compared with several existing methods. 展开更多
关键词 refined local learning neighbor embedding multi-direction non-negative matrix factorization(NMF) POST-PROCESSING
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