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行只包含有维度的账户Row only includes accounts with dimensionality

这就是我说的‘来自高维度的恩赐。That’s why it’s called the blessing of dimensionality.

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他把这一切归功于“来自高维度的恩赐”。He credits such success to “the blessing of dimensionality.”

这也是强大的图像清晰度和三个维度。It is also strong in image clarity and three dimensionality.

但是，强化学习一直被“维数灾难”所困扰。But reinforcement learning is bedeviled by the curse of dimensionality.

维数约简是目标识别的一个重要预处理步骤。Dimensionality reduction is an important preprocessing step before object recognition.

数据的高维度是造成时序数据相似性搜索困难的主要原因。High dimensionality is the main difficulty of similarity search over time-series data.

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教育哲学是杜威思想中最为独特和新颖的维度。Philosophy of education is the most special and novel dimensionality of Dewey s thought.

但人脸的原始特征空间维数很高，无法直接利用LDA方法提取分类特征。But the dimensionality of the original face image is too high to use LDA method directly.

特征选择和特征抽取是维数约简常用的两种方法。Feature selection and feature extraction are common methods for dimensionality reduction.

数据挖掘所面临的挑战在于数据的维数及其容量。The challenge of data mining is dealing with the dimensionality of the data and the volume of it.

再应用多维尺度分析，获得声景观评价影响因素的维度数。Finally, using multidimensional scaling, dimensionality of influencing factors of the soundscape is obtained.

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作为一种新的流形学习框架，扩散映射通过在扩散过程中保持扩散距离进行降维。As a new learning framework, the diffusion method realizes dimensionality reduction in a diffusion processing.

我的论文作品都是基于一个两，三个维度的关系探讨。My paper works have been based around an exploration of the relationship between two and three dimensionality.

在模式识别和图像处理领域中，特征提取是一种特殊的维数降阶的形式。In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.

通过对差异-相似矩阵的处理，最终得到维度较低的文本特征集，并同时生成分类规则。The method generates an item set of low dimensionality and a set of classification rules after dealing with the DSM.

在文本分类中，有效的维数约简可以提高学习任务的效率和分类性能。Effective dimensionality reduction could make the learning task more efficient and more accurate in text classification.

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然而，上世纪90年代中期有一组数学家设计出了一种方法，能够对复杂的数据集进行降维。However, in the mid-90s a group of mathematicians devised a technique for reducing the dimensionality of complex data sets.

而诀窍就是找到一个弹性阵列，能够将非单调的数据集“变成单调数据集”，或者换言之，就是降维。The trick is to find an arrangement of springs that "flattens" the data set, or in other words, reduces its dimensionality.

并提出了针对不同类簇判断有效降维维数的模型选择准则。The model selection principle of determining effective number of dimensionality reduction for different clusters is proposed.