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
随着互联网和面向服务技术的发展,一种新型的Web应用——Mashup服务,开始在互联网上流行并快速增长.如何在众多Mashup服务中找到高质量的服务,已经成为一个大家关注的热点问题.寻找功能相似的服务并进行聚类,能有效提升服务发现的精度...随着互联网和面向服务技术的发展,一种新型的Web应用——Mashup服务,开始在互联网上流行并快速增长.如何在众多Mashup服务中找到高质量的服务,已经成为一个大家关注的热点问题.寻找功能相似的服务并进行聚类,能有效提升服务发现的精度与效率.目前国内外主流方法为挖掘Mashup服务中隐含的功能信息,进一步采用特定聚类算法如K-means等进行聚类.然而Mashup服务文档通常为短文本,基于传统的挖掘算法如LDA无法有效处理短文本,导致聚类效果并不理想.针对这一问题,提出一种基于非负矩阵分解的TWE-NMF(nonnegative matrix factorization combining tags and word embedding)模型对Mashup服务进行主题建模.所提方法首先对Mashup服务规范化处理,其次采用一种基于改进的Gibbs采样的狄利克雷过程混合模型,自动估算主题的数量,随后将词嵌入和服务标签等信息与非负矩阵分解相结合,求解Mashup服务主题特征,并通过谱聚类算法将服务聚类.最后,对所提方法的性能进行了综合评价,实验结果表明,与现有的服务聚类方法相比,所提方法在准确率、召回率、F-measure、纯度和熵等评价指标方面都有显著提高.展开更多
行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用...行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用频谱调谐显著性检测提取显著图,并基于熵值门限进行感兴趣区域的提取;组合非负矩阵分解和方向梯度直方图生成HOG-NMF特征;采用加性交叉核支持向量机方法(Intersection Kernel Support Vector Machine,IKSVM)。该算法显著降低了特征维数,在相同的计算复杂度下明显改善了线性支持向量机的检测率。在INRIA数据库的实验结果表明,该方法对比HOG/线性SVM和HOG/RBF-SVM显著减少了检测时间,并达到了满意的检测率。展开更多
Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimension...Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.展开更多
Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting l...Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.展开更多
This study was on superiority of the non- negative matrix factorization（NMF） algorithm for application of information extracted with aerial images.First,NMF was used for aerial image information extraction,and then ...This study was on superiority of the non- negative matrix factorization（NMF） algorithm for application of information extracted with aerial images.First,NMF was used for aerial image information extraction,and then this data was compared with a principal component analysis（PCA） in which r（the number of rows or columns of basic matrix） and E<sub>ignum</sub>（the number of eigenvalues） were given different values.Experimental results showed that the run time of NMF with r = 20 or 50 was less than that of PCA with an E<sub>ignum</sub> = 20 or 50.Also,the recognition rate of NMF with r = 50 was higher than that of an E<sub>ignum</sub> = 50.The experiment showed that nonnegative matrix factorization had advantages of a short time period with a high recognition rate.展开更多
文摘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.
文摘随着互联网和面向服务技术的发展,一种新型的Web应用——Mashup服务,开始在互联网上流行并快速增长.如何在众多Mashup服务中找到高质量的服务,已经成为一个大家关注的热点问题.寻找功能相似的服务并进行聚类,能有效提升服务发现的精度与效率.目前国内外主流方法为挖掘Mashup服务中隐含的功能信息,进一步采用特定聚类算法如K-means等进行聚类.然而Mashup服务文档通常为短文本,基于传统的挖掘算法如LDA无法有效处理短文本,导致聚类效果并不理想.针对这一问题,提出一种基于非负矩阵分解的TWE-NMF(nonnegative matrix factorization combining tags and word embedding)模型对Mashup服务进行主题建模.所提方法首先对Mashup服务规范化处理,其次采用一种基于改进的Gibbs采样的狄利克雷过程混合模型,自动估算主题的数量,随后将词嵌入和服务标签等信息与非负矩阵分解相结合,求解Mashup服务主题特征,并通过谱聚类算法将服务聚类.最后,对所提方法的性能进行了综合评价,实验结果表明,与现有的服务聚类方法相比,所提方法在准确率、召回率、F-measure、纯度和熵等评价指标方面都有显著提高.
文摘行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用频谱调谐显著性检测提取显著图,并基于熵值门限进行感兴趣区域的提取;组合非负矩阵分解和方向梯度直方图生成HOG-NMF特征;采用加性交叉核支持向量机方法(Intersection Kernel Support Vector Machine,IKSVM)。该算法显著降低了特征维数,在相同的计算复杂度下明显改善了线性支持向量机的检测率。在INRIA数据库的实验结果表明,该方法对比HOG/线性SVM和HOG/RBF-SVM显著减少了检测时间,并达到了满意的检测率。
文摘Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.
文摘Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.
文摘This study was on superiority of the non- negative matrix factorization（NMF） algorithm for application of information extracted with aerial images.First,NMF was used for aerial image information extraction,and then this data was compared with a principal component analysis（PCA） in which r（the number of rows or columns of basic matrix） and E<sub>ignum</sub>（the number of eigenvalues） were given different values.Experimental results showed that the run time of NMF with r = 20 or 50 was less than that of PCA with an E<sub>ignum</sub> = 20 or 50.Also,the recognition rate of NMF with r = 50 was higher than that of an E<sub>ignum</sub> = 50.The experiment showed that nonnegative matrix factorization had advantages of a short time period with a high recognition rate.
