Nonlinear dimensionality reduction of large datasets for data exploration

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Dimensionality reduction techniques are outlined; their strengths and limitations are discussed. The novel dimensionality reduction method is presented, which is a combination of input space approximation, nonlinear dimensionality reduction and function approximation techniques. The method is especially useful for large scale real-world datasets, where existing methods fail to succeed because of extreme computational expenses. The method can be used in exploratory data analysis and aims to create low dimensional data representation for better data structure understanding and for cluster analysis. The comparison of dimensionality reduction techniques is performed in order to justify the applicability of the proposed method.