Musa, A.B. (2013) A Comparison of ?1-Regularizion, PCA, KPCA and ICA for Dimensionality Reduction in Logistic Regression. International Journal of Machine Learning and Cybernetics.

has been cited by the following article:

• 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.