Estimation of Intrinsic Dimension using Supervised Parameter Selection Method
Haiquan Qiu, Shulun Yang · Journal of Physics Conference Series · 2019
Abstract In this paper, we propose a new method for estimating the intrinsic dimension of datasets. The new method uses the local information of different scales of the sample points (by adjacency matrix) to estimate the intrinsic dimension. The only parameter used in the new method is the scaling ratio k, which determines the adjacency matrix of different scales. We propose a parameter selection method based on the difference of estimated dimension and the classification accuracy of projection data. Experiments on real datasets demonstrate the effectiveness of the proposed method.