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
针对免疫入侵检测数据处理速度慢以及检测实时性差的问题,提出Bregman非负矩阵分解算法,采用Bregman迭代方式改进传统非负矩阵分解过程,优化矩阵迭代过程,利用矩阵本地化方法分解矩阵,增加矩阵的约束,保留检测数据内部结构并且加快数据...针对免疫入侵检测数据处理速度慢以及检测实时性差的问题,提出Bregman非负矩阵分解算法,采用Bregman迭代方式改进传统非负矩阵分解过程,优化矩阵迭代过程,利用矩阵本地化方法分解矩阵,增加矩阵的约束,保留检测数据内部结构并且加快数据的处理速度。在KDD CUP 1999数据集上的仿真结果表明,该算法有效提高了入侵检测速度,增强了免疫入侵检测的时效性。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.
基金国家自然科学基金(62272077,72301050)重庆市教委科技重大项目(KJZD-M202400604)资助+1 种基金the Natural ScienceFoundation of Chongqing,China(No.cstc2021jcyj msxmX0557)the MOE Layout Foundation of Humanities and Social Sciences,China(No.20YJAZH102).
文摘针对免疫入侵检测数据处理速度慢以及检测实时性差的问题,提出Bregman非负矩阵分解算法,采用Bregman迭代方式改进传统非负矩阵分解过程,优化矩阵迭代过程,利用矩阵本地化方法分解矩阵,增加矩阵的约束,保留检测数据内部结构并且加快数据的处理速度。在KDD CUP 1999数据集上的仿真结果表明,该算法有效提高了入侵检测速度,增强了免疫入侵检测的时效性。
