Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532719 | Pattern Recognition | 2009 | 8 Pages |
Abstract
Dimensionality reduction is a very important tool in data mining. Intrinsic dimension of data sets is a key parameter for dimensionality reduction. However, finding the correct intrinsic dimension is a challenging task. In this paper, a new intrinsic dimension estimation method is presented. The estimator is derived by finding the exponential relationship between the radius of an incising ball and the number of samples included in the ball. The method is compared with the previous dimension estimation methods. Experiments have been conducted on synthetic and high dimensional image data sets and on data sets of the Santa Fe time series competition, and the results show that the new method is accurate and robust.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Mingyu Fan, Hong Qiao, Bo Zhang,