FRAPPE: \(\underline{\text{F}}\)ast \(\underline{\text{Ra}}\)nk \(\underline{\text{App}}\)roximation with \(\underline{\text{E}}\)xplainable Features for Tensors

Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key parameter: the number of desired components. In the case of the CANDECOMP/PARAFAC decomposition (CPD), this value is known as the canonical rank and gre...

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Veröffentlicht in:	arXiv.org 2022-06
Hauptverfasser:	Shiao, William, Papalexakis, Evangelos E
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Bayesian analysis Decomposition Heuristic methods Mathematical analysis Multidimensional data Multidimensional methods Tensors
Online-Zugang:	Volltext
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