Restricted Boltzmann machine: a non-linear substitute for PCA in spectral processing
Yude Bu, Gang Zhao, A-Li Luo, Jingchang Pan, Yuqin Chen · Astronomy and Astrophysics · 2015
Context. Principal component analysis (PCA) is widely used to repair incomplete spectra, to perform spectral denoising, and to reduce dimensionality. Presently, no method has been found to be comparable to PCA on these three problems. New methods have been proposed, but are often specific to one problem. For example, locally linear embedding outperforms PCA in dimensionality reduction. However, it cannot be used in spectral denoising and spectral reparing. Wavelet transform can be used to denoise spectra; however, it cannot be used in dimensionality reduction.