Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5466791 | Ultramicroscopy | 2017 | 10 Pages |
Abstract
Principal component analysis (PCA) is among the most commonly applied dimension reduction techniques suitable to denoise data. Focusing on its limitations to detect low variance signals in noisy data, we discuss how statistical and systematical errors occur in PCA reconstructed data as a function of the size of the data set, which extends the work of Lichtert and Verbeeck, (2013) [16]. Particular attention is directed towards the estimation of bias introduced by PCA and its influence on experiment design. Aiming at the denoising of large matrices, nullspace based denoising (NBD) is introduced.
Related Topics
Physical Sciences and Engineering
Materials Science
Nanotechnology
Authors
Jakob Spiegelberg, Ján Rusz,