A novel trilinear decomposition algorithm for second-order linear calibration

By Zeng‐Ping Chen, Hai-Long Wu, Jian-hui Jiang, Yang Li, R. Yu. 2000

184 citations recorded by the source corpus.

How researchers see this paper

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

A comparison of algorithms for fitting the PARAFAC model (2006, methodology): “Recently, several algorithms (ASD (Jiang et al., 2000), SWATLD (Chen et al., 2000) ATLD (Wu et al., 1998), PALS (Chen et al., 2001), ACOMAR (Li et al., 2000), ACOVER (Jiang et al., 1999)) based on ideas similar to PARAFAC-ALS have been proposed.”
A closed‐form expression for computing the sensitivity in second‐order bilinear calibration (2005, methodology): “PARAFAC, SWATLD, GRAM and MCR-ALS operate according to Figure 1(A), whereas BLLS/RBL and PLS/RBL employ the scheme of Figure 1(B).”
A scalable optimization approach for fitting canonical tensor decompositions (2011, unknown): “…[14–20],alternatingtrilineardecomposition(ATLD)[21],self-weightedalternatingtrilineardecomposition(SWATLD)[22,23], pseudo-alternatingleast-squares(PALS)[24],alternatingcoupled…”
Unsupervised Multiway Data Analysis: A Literature Survey (2009, background): “Another comparative study on PARAFAC algorithms is [57], which compares some other algorithms in addition to PARAFAC-ALS, SWATLD, and ASD from [54].”