Maximum likelihood fitting using ordinary least squares algorithms

By Rasmus Bro, N. D. Sidiropoulos, Age K. Smilde. 2002

95 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4407f78bf886924005d237abae41d3b3775c6546

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. Tamara G. Kolda
2. Evrim Acar
3. Brett W. Bader
4. Bülent Yener

In Environmental Engineering

1. Evrim Acar
2. Bülent Yener

In Statistics and Probability

1. Tamara G. Kolda
2. Evrim Acar
3. Brett W. Bader
4. Bülent Yener

Citations in the corpus

• Unsupervised Multiway Data Analysis: A Literature Survey (2009, methodology): “This approach has later been generalized to model data, which might have correlated measurement errors, in [Bro et al. 2002] by an algorithm called Maximum likelihood via Iterative Least Squares (MILES).”
• Tensor Decompositions and Applications (2009, background): “Bro, Sidiropoulos, and Smilde [35] and Vega-Montoto and Wentzell [234] formulated maximum-likelihood versions of CP. Acar and Yener [6] discuss several of these decompositions as well as some not covered here: shifted CP and Tucker [97] and convoluted CP [171].”