Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tuck...

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Veröffentlicht in:	Numerical linear algebra with applications 2022-12, Vol.29 (6), p.n/a
Hauptverfasser:	Alexandrov, Boian, DeSantis, Derek F., Manzini, Gianmarco, Skau, Erik W.
Format:	Artikel
Sprache:	eng
Schlagworte:	data compression Decomposition Factorization Feature extraction linear constraints Machine learning Mathematical analysis minimal cones nonnegative CANDELINC nonnegative multirank nonnegative rank nonnegative Tucker Tensors
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