A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression  

作　　者：Azza Kamal Ahmed Abdelmajed Azza Kamal Ahmed Abdelmajed(Department of Computer Sciences, Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan)

机构地区：[1]Department of Computer Sciences, Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan

出　　处：《Journal of Data Analysis and Information Processing》2016年第2期55-63,共9页数据分析和信息处理（英文）

摘　　要：There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.

关 键 词：Logistic Regression (LR) Principal Component Analysis (PCA) Locality Preserving Projection (LPP) 

分 类 号：O17[理学—数学]