主成分分析毕业论文英文版

一楼的翻译有问题 注意““十一五”期间”是专指 产业结构不能翻译成工业结构（The industrial structure） 错误百出 下面就原文翻译如下：（花费了我很长时间哦好好看对你有帮助的 ） During the period of "11th Five-Year Plan", to implement the State Scientific Concept of Development, as the realization of changing the mode of economic growth must be the strategic restructuring of the economy as the focus of economic work. Corporate Structure and in the economic structure in the main, based in part, corporate structure of the adjustment and upgrading of relations with the adjustment of economic structure, economic relations can not be sustained, steady and healthy development. The article each with "Shift Share Analysis", "principal component analysis", "The structure of conversion coefficient" to quantify the regional provinces of the corporate structure and efficiency as well as its ability to convert it conversion Speed. According to the analysis of the results of each provinces in the region's corporate structure to classify and evaluation.

第三，该模型估计为子数据集的样本来说明同种的好产业和有差别的好产业，引述金姆和马里昂（1997年）。最后，该对模型进行测试，纠正实验问题，如同时性和异方差;主成分的分析技术适用于调整共线性。

Eleventh Five-Year "period, China's science development concept should be implemented to achieve the economic growth pattern, it must be strategic restructuring of the economy as the focus of economic work. The economic structure of the industrial structure is the most important, the most basic part of , the industrial structure adjustment and upgrading of the relationship between the economic structure adjustment, the relationship between the economy sustained, steady and healthy development. In this article were used to deviate from the share analysis, principal component analysis, the structure of conversion factors to quantify the provinces domain industrial efficiency, industrial conversion capacity, the conversion rate of the industrial structure. And in accordance with the results of the analysis domain of the provincial industrial classification and evaluation.

你的邮箱发不进去，请换一个，这里发部分供你参考Principal component analysisPrincipal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has as high a variance as possible (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen–Loève transform (KLT), the Hotelling transform or proper orthogonal decomposition (POD).PCA was invented in 1901 by Karl Pearson.[1] Now it is mostly used as a tool in exploratory data analysis and for making predictive models. PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of a PCA are usually discussed in terms of component scores (the transformed variable values corresponding to a particular case in the data) and loadings (the weight by which each standarized original variable should be multiplied to get the component score) (Shaw, 2003).PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-dimensional data space (1 axis per variable), PCA can supply the user with a lower-dimensional picture, a "shadow" of this object when viewed from its (in some sense) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is is closely related to factor analysis; indeed, some statistical packages (such as Stata) deliberately conflate the two techniques. True factor analysis makes different assumptions about the underlying structure and solves eigenvectors of a slightly different matrix.

第三，该模型估计的子数据集的样本占齐好产业和良好的产业分化，继Kim和马里昂（1997年）。最后，该模型进行测试，如同时性和异方差经验问题纠正;的主成分分析技术应用到调整多重共线性。