Musings on multilinear fitting

50 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 literature survey of low‐rank tensor approximation techniques (2013, neutral): “More recent developments for general tensors include work on increasing the efficiency and robustness of gradient-based and Newton-like methods [2,73,75,141,224,225], modifying and improving ALS (alternating least squares) [42, 58, 59, 91, 226], studying the convergence of ALS [52, 195, 255] and red”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “The convergence of ALS is not yet completely understood — it is quasi-linear close to the stationary point [55], while it becomes rather slow for ill-conditioned cases; for more detail we refer to [56], [57].”
Tensor Decomposition for Signal Processing and Machine Learning (2016, background): “As the musings in [84] make clear, understanding the convergence properties of ALS is highly nontrivial.”
Zur Theorie der Niedrigrangapproximation in Tensorprodukten von Hilberträumen (2013, background): “Als eine der ersten Arbeiten, in denen die Idee, hochdimensionalen Problemen in dieser Weise zu begegnen, allgemein formuliert ist, wird oft Beylkin u. Mohlenkamp [7] genannt.”