Nesterov-based alternating optimization for nonnegative tensor factorization: algorithm and parallel implementation
- Λιαβας Αθανασιος(http://users.isc.tuc.gr/~aliavas)
- Liavas Athanasios(http://users.isc.tuc.gr/~aliavas)
- Κωστουλας Γεωργιος(http://users.isc.tuc.gr/~gkostoulas)
- Kostoulas Georgios(http://users.isc.tuc.gr/~gkostoulas)
- Λουρακης Γεωργιος(http://users.isc.tuc.gr/~glourakis)
- Lourakis Georgios(http://users.isc.tuc.gr/~glourakis)
- Huang Kejun()
- Σιδηροπουλος Νικολαος(http://users.isc.tuc.gr/~nsidiropoulos)
- Sidiropoulos Nikolaos(http://users.isc.tuc.gr/~nsidiropoulos)
DOI Publisher
Institute of Electrical and Electronics Engineers (IEEE)Abstract
Summarization: We consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization framework and solve each matrix nonnegative least-squares problem via a Nesterov-Type algorithm for strongly convex problems. We describe a parallel implementation of the algorithm and measure the attained speedup in a multicore computing environment. It turns out that the derived algorithm is a competitive candidate for the solution of very large-scale dense nonnegative tensor factorization problems.Presented on
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