What is the translation of " DIMENSIONALITY REDUCTION " in Chinese?

• 降维
• 维数降低
• 维数约简

Examples of using Dimensionality reduction in English and their translations into Chinese

Like clustering methods, dimensionality reduction seeks an inherent structure in the data.
与聚类方法一样，维数减少也是为了寻求数据的固有结构。

True or False It is not necessary to have a target variable for applying dimensionality reduction algorithms.
真或假没有必要有一个用于应用维数降低算法的目标变量。

Dimensionality reduction is essential for coping with big data- like the data coming in through your senses every second.
维数约简对于应对大数据（像每秒钟通过你的知觉而进入的数据）来说很关键。

In this post I will do my best to demystify three dimensionality reduction techniques; PCA, t-SNE and Auto Encoders.
在这篇文章中，我将尽我所能揭秘三种降维技术：PCA、t-SNE和自编码器。

In this video, I would like to start talking about a second type of unsupervised learning problem called dimensionality reduction.
这个视频，我想开始谈论第二种类型的无监督学习问题，称为降维。

Dimensionality Reduction is a technique that allows one to map multidimensional data to a Key-Value model or to other non-multidimensional models.
降维这种技术允许将一个多维数据模型映射到一个键-值模型或其他非多维模型。

In this article, we discussed the advantages of PCA for feature extraction and dimensionality reduction from two different points of view.
在这篇文章中，我们从两个观点讨论了PCA对特征提取和降维的优点。

Today data denoising and dimensionality reduction for data visualization are considered as two main interesting practical applications of autoencoders.
当前，数据去噪和数据可视化中的降维被认为是自编码器的两个主要的实际应用。

Sample application demonstrating how to use Principal Component Analysis(PCA) to perform linear transformations and dimensionality reduction.
示例应用程序演示如何使用主成分分析（PCA）来执行线性变换和维数约简。

Autoencoders are related to PCA and other dimensionality reduction techniques, but can learn more complex mappings due to their nonlinear nature.
自编码器和PCA等降维技术相关，但因为它们的非线性本质，它们可以学习更为复杂的映射。

It can handle thousands of input variables and identify most significant variables so it is considered as one of the dimensionality reduction methods.
它可以处理数千个输入变量并识别最重要的变量，因此它被认为是降维方法之一。

Dimensionality reduction, which refers to the methods used to represent data using less columns or features, can be accomplished through unsupervised methods.
降维指的是使用较少的列或特征来表示数据的方法，可以通过无监督的方法来实现。

For instance, the price of a house might be correlated with its location so the dimensionality reduction algorithm will merge them into one feature.
比如，一辆车的里程可能跟它的车龄相关，所以降维算法就会把它们合并成一个表示汽车磨损的特征。

Consider performing dimensionality reduction(PCA, ICA, or Feature selection) beforehand to give your tree a better chance of finding features that are discriminative.
考虑事先进行降维PCA,ICA，使您的树更好地找到具有分辨性的特征。

But this is not the full functionality of Scikit-learn, it can also be used to do dimensionality reduction, clustering, whatever you can think of.
但这并不是Scikit-learn的全部功能，它同样可以用来做降维，聚类等等任何你所能想到的。

Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself.
降维是无监督算法的另一个例子，其中标签或其他信息是从数据集本身的结构推断的。

This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.
这个传统的框架是用Python编写的，并且包含了几种机器学习模型，包括分类，回归，聚类和降维。

Dimensionality reduction techniques allow us to make data more comfortable to use and often remove noise to build other machine learning tasks more accurate.
降维技术使得数据集变得更容易使用，并且它们往往能够去除数据中的噪声，使得其他机器学习任务更加精确。

Generally speaking, the number of dimensions must be reduced through techniques such as hierarchical aggregation, dimensionality reduction(like PCA and LDA), and dimensional subsetting.
一般来讲，维度数目必须通过技术（比如，层次聚合，降维（例如，PCA和LDA）和维度裁剪）来减少。

Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, in order to summarize or describe data using less information.
和集簇方法类似，降维追求并利用数据的内在结构，目的在于使用较少的信息总结或描述数据。

There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction.
书中有详细的示例和现实世界的用例，并探索常见的机器学习模型，包括推荐系统，分类，回归，聚类和降维。

Dimensionality Reduction is a technique that allows one to map multidimensional data to a Key-Value model or to other non-multidimensional models.
DimensionalityReduction降维是一种技术可以允许把一个多维的数据映射成一个Key-Value或是其它非多给的数据模型。

The Embedding Projector offers three commonly used methods of data dimensionality reduction, which allow easier visualization of complex data: PCA, t-SNE and custom linear projections.
EmbeddingProjector提供了三种常用的数据维数降低方法，允许更容易地显示复杂数据：PCA、t-SNE和定制线性投影。

Running a dimensionality reduction algorithm such as PCA prior to k-means clustering can alleviate this problem and speed up the computations.
在k-means聚类之前运行诸如PCA之类的dimensionalityreductionalgorithm（降维算法）可以减轻这个问题并加快计算速度。

To understand the use of LDA in dimensionality reduction, it is useful to start with a geometric reformulation of the LDA classification rule explained above.
为了理解LDA在降维上的应用，我们从上面解释的LDA分类规则的几何重构(geometricreformulation)开始说起是十分有用的。

Dimensionality Reduction: True to its name, Dimensionality Reduction means reducing the number of variables of a dataset while ensuring that important information is still conveyed.
降维：正如它的名字所示，降维意味着减少数据集中变量的个数，但是仍然保留重要的信息。

Consider performing dimensionality reduction(PCA, ICA, or Feature selection) beforehand to give your tree a better chance of finding features that are discriminative.
考虑事先对数据进行降维(PCA,ICA)，使您的树能更好地找到具有分辨性的特征。

Dimensionality Reduction: True to its name, Dimensionality Reduction means reducing the number of variables of a dataset while ensuring that important information is still conveyed.
维度减少：正如其名称，维度减少意味着减少数据集的变量数量，同时确保重要的信息仍然传达。

Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, in order to summarize or describe data using less information.
如同聚类方法，降维方法试图利用数据中的内在结构来总结或描述数据，所不同的是它以无监督的方式利用更少的信息。