1) Random Projected Clustering
随机投影聚类
1.
A Random Projected Clustering Algorithm Facing High-Dimensional Categorical Data;
一种面向高维符号数据的随机投影聚类算法
2) random projection
随机投影
1.
Method of image reconstruction based on very sparse random projection;
基于非常稀疏随机投影的图像重建方法
2.
Random Projection Based Clustering Method of Parallel Data Streams
基于随机投影的并行数据流聚类方法
3.
In this paper,sub-Gaussian random projection is introduced into compressed sensing(CS) theory and two new kinds of CS measurement matrix:sparse projection matrix and very sparse projection matrix are presented.
将亚高斯随机投影引入可压缩传感CS(compressed sensing)理论,给出了两种新类型的CS测量矩阵:稀疏投影矩阵和非常稀疏投影矩阵。
3) projective clustering
投影聚类
1.
In order to successfully find the subspaces,Agrawal proposes the conception of projective clustering,converting the data into subspaces with mapping and using another method to find clusters.
为找出有效的子空间,Agrawal等人提出了投影聚类概念,通过映射变换转换到子空间里,然后借助其他方法找到聚类。
2.
In this paper,a new Parzen-Window-based subspace dividing method is given,and accordingly,the new projective clustering algorithm PCPW is proposed.
在此提出了一种新的划分子空间方法——基于Parzen窗子空间划分方法,并在这基础上提出了新的投影聚类方法PCPW。
4) projected clustering
投影聚类
1.
Most of the existing methods perform the projected clustering for gene expression data under the assumption of independence among genes and choose relevant projected space according to individual discriminative score of single gene.
针对现有基因表达数据投影聚类算法假定基因相互独立,根据每个基因的独立区分度选择相关投影空间的不足,提出了根据基因间相互关系进行投影聚类的算法MOLION。
2.
To address the deficiencies of most existing gene clustering algorithms,a novel gene projected clustering algorithm is proposed.
针对现有基因投影聚类算法的不足,提出一种有效的基因投影聚类算法。
5) stochastic clustering
随机聚类
1.
Study of segmentation technique for colonscopic image based on stochastic clustering;
基于随机聚类的结肠镜图像分割技术的研究
2.
A stochastic clustering method based on pairwise similarity of elements was utilized to cluster feature space.
利用基于元素间相似性的随机聚类方法对特征空间进行聚类。
6) soft projected clustering
软投影聚类
补充资料：滤波反投影或卷积反投影
滤波反投影或卷积反投影
影像学术语。当代影像学设备进行影像重建的数学方法。在直接用扫描后所获得的投影轨迹剖面图反投影重建出的CT图像中，无法避免角度卷入条纹伪影(angular aliasing streaks)造成的模糊和失真。这种现象与被扫描层面的空间频率中高频信息的损失有关。使用一种精密的数学方法去除这种模糊。称为“展现”(unfolding)或去卷积(deconvolution)，即在反投影前使用一种数学的“滤器”或卷积函数对原始数据进行修正，然后再进行反投影。两步数学处理过程合称为滤波(修正后)反投影或卷积(后)反投影。这种方法的优点是处理过程简单，速度快，所得图像逼真、清晰。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条