Open AccessFeature Extraction Using Discriminant Graph Laplacian Principal Component Analysis with Application to Biomedical Datasets
Author(s) -
Muhammad Aminu,
Noor Hazlina Ahmad
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1372/1/012002
Subject(s) - subspace topology , discriminative model , principal component analysis , pattern recognition (psychology) , discriminant , artificial intelligence , graph , margin (machine learning) , computer science , laplacian matrix , feature extraction , linear discriminant analysis , dimensionality reduction , laplace operator , nonlinear dimensionality reduction , manifold alignment , mathematics , machine learning , theoretical computer science , mathematical analysis
In this paper, we propose a manifold learning method called discriminant graph Laplacian principal component analysis (DGLPCA) for feature extraction. The proposed method projects high dimensional data into a lower dimensional subspace while preserving much of the intrinsic structure of the data. Moreover, DGLPCA integrates maximum margin criterion into its objection function to improve class separability in the lower dimensional space. The effectiveness of the proposed method is demonstrated on two publicly available biomedical datasets taken from UCI machine learning repository. The results show that our proposed method provides more discriminative power compared to other similar approaches.