Principles and methodologies in self-modeling curve resolution

By Jian-hui Jiang, Yizeng Liang, Y. Ozaki. 2004

152 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/7f77aceb30eed598638728c60ddc6ddc55914167

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Shira Kritchman
2. Boaz Nadler
3. Róbert Rajkó
4. David J. Biagioni

In Industrial and Manufacturing Engineering

1. Boaz Nadler
2. Shira Kritchman
3. Róbert Rajkó

In Spectroscopy

1. Boaz Nadler
2. Shira Kritchman
3. Róbert Rajkó

Citations in the corpus

• Studies on the adaptability of different Borgen norms applied in self‐modeling curve resolution (SMCR) method (2009, methodology): “Although Jiang and Ozaki [9] and Jiang et al. [ 12 ] gave a proof that the normalized (spectra or concentration) profiles are embraced in the (N � 1)-dimensional simplex with the vertices being the normalized profiles of the N pure components; their derivation was not fully satisfactory, because for”
• Natural duality in minimal constrained self modeling curve resolution (2006, background): “However, these decompositions are ambiguous [6–18] in the sense of least squares (of course, the solution of either PCA or SVD is unique because of the used restrictions of orthogonality, normalization, sign (e.”
• Determining the number of components in a factor model from limited noisy data (2008, methodology): “In this paper we present a new method to automatically determine the number of components from a limited number of (possibly) high dimensional noisy samples.”