(Coordinate Descent for Tensor Factorization) and SALS
(Subset Alternating Least Square) are
tensor factorization algorithms for a high-order and large-scale tensor
parallelizable in distributed environments
scalable with the order and size of data; the number of parameters; and the number of machines
require several orders of magnitude less memory space than their competitors
has an advantage in terms of memory usage and flexibility, while SALS
has an advantage in terms of convergence speed
are described in the following papers:
Distributed Methods for High-dimensional and Large-scale Tensor Factorization.
Kijung Shin and U Kang.
IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China
Fully Scalable Methods for Distributed Tensor Factorization.
Kijung Shin, Lee Sael, and U Kang
IEEE Transactions on Knowledge and Data Engineering (TKDE) 2016 (To Appear)
[PDF] [Supplementary Document] [BIBTEX]
The source codes used in the papers are available.
[ver.1 (ICDM)] [ver.2 (TKDE)]
CDTF Hadoop version & single machine (multi-thread) version
SALS Hadoop version & single machine (multi-thread) version