Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Geodesic entropic graphs for dimension and entropy estimation in manifold learning
IEEE Transactions on Signal Processing
Hi-index | 0.00 |
The spectra of color can represent a color in the most accurate way, but the dimension of the spectral data is too high to process. This paper aims to analyze the spectral reflectance curves of 1269 Munsell standard color samples with some influential algorithms in manifold learning. Experimental results reveal that the intrinsic dimension of the embedded manifold in the spectral Munsell color space is 3 and the 3-dimensional structure of this manifold looks like a cone, consistent with the development and structure of the Munsell color system.