TITLE:
The Discriminate Analysis and Dimension Reduction Methods of High Dimension
AUTHORS:
Lan Fu
KEYWORDS:
High-Dimensional Data, Method of Dimensionality Reduction, Discriminant Analysis, Linear Dimensionality Reduction, Manifold Structure
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.3 No.3,
March
18,
2015
ABSTRACT: Statistical methods have been getting constant
development since 1970s. However, the statistical methods of the big data are no
longer restricted with these methods which are listed in the textbook. This paper
mainly demonstrates the Discrimination Analysis of Multivariate Statistical Analysis,
Linear Dimensionality Reduction and Nonlinear Dimensionality Reduction Method under
the circumstances of the wide range of applications of high-dimensional data. This
paper includes three parts. To begin with, the paper illustrates a developing trend
from the data to the high-dimensional. Meanwhile, it analyzes the impacts of the
high-dimensional data on discriminate analysis methods. The second part represents
the necessity of the dimensionality reduction studies in the era of the high-dimensional
data overflowing. Then, the paper focuses on introducing the main methods of the
linear dimensionality reduction. In addition, this paper covers the basic idea of
the nonlinear dimensionality reduction. Moreover, it systematically analyzes the
breakthrough of the traditional methods. Furthermore, it chronologically demonstrates
the developing trend of the dimensionality reduction method. The final part shows
a comprehensive and systematic conclusion to the whole essay and describes a developing
prospect of the dimension reduction methods in the future. The purpose of this essay
is to design a framework of a performance system which is subject to the characteristics
of China High-tech enterprises. It based on the analysing the principles and significance
of the performance system of High-tech enterprises. The framework will promote the
standardize management of High-tech enterprises of China.