TITLE:
A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression
AUTHORS:
Azza Kamal Ahmed Abdelmajed
KEYWORDS:
Logistic Regression (LR), Principal Component Analysis (PCA), Locality Preserving Projection (LPP)
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.4 No.2,
May
12,
2016
ABSTRACT: 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.