Comparative study of dimensionality reduction techniques for data visualization

Abstract

This study analyzed current linear and nonlinear dimensionality reduction techniques in the context of data visualization. A summary of current linear and nonlinear dimensionality reduction techniques was presented. Linear techniques such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) are good for handling data that is inherently linear in nature. Nonlinear techniques such as Locally Linear Embedding (LLE), Hessian LLE (HLLE), Isometric Feature Mapping (Isomap), Local Tangent Space Alignment (LTSA), Kernel PCA, diffusion maps and multilayer autoencoders can perform well on nonlinear data. Experiments were conducted on varying the neighborhood, density and noise of data. Results on two real-world datasets (ORL face and business blogs) indicate that dimensionality reduction techniques generally performed better on the synthetic data. In our experiments, the best performing algorithm overall for both real-world and artificial data was Isomap. © 2010 Asian Network for Scientific Information.