期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 24, 期 1, 页码 59-71出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2010.225
关键词
Intrinsic dimension; fractal dimension; feature selection; knowledge discovery in databases
类别
资金
- US National Science Foundation (NSF) [0555962, 0825710]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [0555962, 0825710] Funding Source: National Science Foundation
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.
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