Most text classifications reduce dimensionality by using feature selection that can choose a subset from the original feature set according to some criterions, this method may neglect some relevant factor.
现有的文本自动分类中的降维大多采用特征选择的方法，选择一些主要特征，即通过评价函数进行降维，但通过这种方法选择的特征项中可能还包含一些彼此相关的因素，也就是说有些特征是冗余的。
参考来源 - 基于投影寻踪中文网页自动分类·2,447,543篇论文数据，部分数据来源于NoteExpress
Multidimensional scaling to simplify multidimensional data is an attempt "to reduce the dimensionality of data by finding key attributes defining most of the behavior, " says Venkatasubramanian.
用文卡的话说，简化多维数据的多维标度就是试图“通过找出定义大多数行为的关键属性来降低数据的维数”。
The algorithm is impractical on large data sets, unless it USES dimensionality reduction, sampling, or partitioning - all of which reduce recommendation quality.
在大数据集的情况下，这样的算法不可行，除非使用维度降低、抽样或区隔——所有这些都降低了推荐的品质。
It has been developed with an aim to reduce or eliminate information bearing secondary importance, and retain or highlight meaningful information while reducing the dimensionality of data.
其目的是在减少数据维数的同时，尽量减少或去除次要的冗余信息，并且保留或增强有意义的信息。
应用推荐