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