Efficient classification scheme based on hybrid global and local properties of feature
Heesung Lee, Sungjun Hong, Sungje An, Euntai Kim · 2008
This paper proposes a new pattern classification scheme, combining global and local features. The proposed method uses principal component analysis (PCA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA is known for preserving the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. Our combing method integrates global and local descriptive information and finds a richer set of alternatives beyond PCA and LPP in a 2-D parametric space. In order to find the hybrid features adaptively and find optimal parameters, we employ the genetic algorithm (GA). Experiments are performed with UCI machine learning repository to show the performance of the proposed algorithm.