Fractal-Based Intrinsic Dimension Estimation and Its Application in Dimensionality Reduction
Dimensionality reduction is an important step in knowledge discovery in databases. Intrinsic dimension indicates the number of variables necessary to describe a data set. Two methods, box-counting dimension and correlation dimension, are commonly used for intrinsic dimension estimation. However, the robustness of these two methods has not been rigorously studied. This paper demonstrates that correlation dimension is more robust with respect to data sample size. In addition, instead of using a user selected distance d, we propose a new approach to capture all log-log pairs of a data set to more precisely estimate the correlation dimension. Systematic experiments are conducted to study factors that influence the computation of correlation dimension, including sample size, the number of redundant variables, and the portion of log-log plot used for calculation. Experiments on real-world data sets confirm the effectiveness of intrinsic dimension estimation with our improved method. Furthermore, a new supervised dimensionality reduction method based on intrinsic dimension estimation was introduced and validated.
intrinsic dimension knowledge discovery in databases fractal dimension feature selection