IMD-Isomap for Data Visualization and Classification
Gu Ruijun, Wenbo Xu, Bin Ye · 2007
In recent years, some nonlinear dimension reduction methods, named manifold learning, have been proposed and widely used in data visualization and pattern recognition. Of them, Isomap is a representative, which can project data from high-dimensional space into low-dimensional space with local structure preserved perfectly. However, Isomap suffers from the topological stability and is sensitive to noise. Moreover, it can only run in a batch mode, so cannot be directly used in pattern classification. In this paper, firstly, an improved Isomap based on image distance, namely IMD-Isomap, is proposed. Because spatial information of images is considered in image distance, as our experiments will show, IMD-Isomap outperforms Isomap for data visualization especially when noise is added. Then, combining IMD-Isomap and generalized regression neural network, which has a good ability for approximation, a classification method is proposed. Experimental results showed that our methods are robust to noise for data visualization or image classification when compared with KNN, Isomap or eigenface.