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1·One of the steps of data preprocessing is dimensionality reduction.

维数约简是数据预处理的步骤之一。

2·A novel method for dimensionality reduction of kernel matrix is presented.

提出了基于聚类的核矩阵维度缩减技术。

3·Feature selection and feature extraction are common methods for dimensionality reduction.

特征选择和特征抽取是维数约简常用的两种方法。

4·In this paper we present an improved dimensionality reduction method based on support vector machines.

提出了一种基于支持向量机的改进的降维方法。

5·Effective dimensionality reduction could make the learning task more efficient and more accurate in text classification.

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。

6·Multidimensional scaling is a powerful tool for dimensionality reduction in the field of pattern recognition and data mining.

多维尺度分析是模式识别与数据挖掘领域一个有力的降维工具。

7·The model selection principle of determining effective number of dimensionality reduction for different clusters is proposed.

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。

8·The original nonlinear dimensionality reduction algorithms are non-supervised, which can't directly be applied in pattern recognition.

原始的非线性维数约减算法是无监督的，不能直接用于模式识别。

9·The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.

局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。

10·A new dimensionality reduction method for calculating the radiant heat transfer with two dimensional characteristics was introduced in this paper.

针对具有二维特征的辐射传热问题，介绍了一种降维方法。