数据向量 meaning in Chinese
data vector
Examples
- Experiments on a large real - world dataset demonstrate a remarkable reduction of the amount of accessed vectors in exact nn searches compared with existing indexing schemes
和现有的精确索引机制相比，可以显著减少检索时需要访问数据向量的次数。 - Different from previous document clustering method based on nmf , our methods try to discover both the geometric and discriminating structures of the document space in an unsupervised manner , companied with high accuracy in acceptable computationally expensive
与基于nmf算法的文本聚类不同,我们的算法力求以无监督的方式,在时间复杂度允许的范围内,找到更适合于分类操作的数据向量间的几何局部特征向量及相应的各文档的编码向量。 - The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality . so the primary step in document clustering is to project the document into a lower - rank semantic space in which the documents related to the same semantics are close to each other
基于文本空间的文本聚类因为其具有高维的特征而不容易直接实现,所以文本聚类的首要步骤就是将文本空间的数据投影到较低维的语义空间里,使在文本空间里相邻的数据向量在语义空间里根据某些提取的特征参数而相似。 - Different from other rank reduction methods , such as pca ( principal component analysis ) and vq ( vector quantization ) , nmf ( nonnegative matrix factorization ) can get nonnegative , sparse basis vectors which make possible of the concept of a parts - based representation
与pca (主分量分析)和vq (矢量量化)等降维算法不同, nmf (非负矩阵分解)算法能够分解出非负的,稀疏的特征矩阵和编码矩阵,能够提取原始数据向量的局部特征,使基于局部特征进行分类的聚类算法更容易实现。