This paper introduces a newly generalized and dynamic structure for the similarity retrieval of high dimensional feature vectors called the recursive clustering index tree.
文章提出了一种新的适用于高维特征矢量相似检索动态聚类索引树结构。
In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.
该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。
The concepts of high attribute dimensional information system are firstly proposed, and a new dynamic clustering method on the basis of sparse feature difference degree is presented.
针对高属性维稀疏数据聚类问题，提出高属性维稀疏信息系统概念，给出一种新的基于稀疏特征差异度的动态抽象聚类方法。
Then, in the light of high-resolution radar target one dimensional range profiles, it is decomposed in multi-resolution and the multi-resolution feature description are obtained.
然后提出了可用于高距离分辨力雷达目标识别的一维距离象多分辨分解和目标结构特征的多分辨描述方法。
The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed.
所接收的混合信号首先被映射到高维的内核特征空间，和由内核特征空间上的适应度函数给出的特征矢量的基础构造。
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
低层视觉特征提取、高维数据索引机制和相关反馈方法是面向大规模图像库基于内容检索的三个关键问题。
Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.
文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。
The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting.
概念流动的出现及数据的高维性增加了数据流特征选择的复杂性。
The complexity of feature selection for real-world data stream will increase because of high-dimensional data and concept drifting.
概念流动的出现及数据的高维性增加了数据流特征选择的复杂性。
应用推荐