SIAM Journal on Computing
Tensor-CUR decompositions for tensor-based data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A first look at modern enterprise traffic
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Cross-language information retrieval using PARAFAC2
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiway analysis of epilepsy tensors
Bioinformatics
Efficient MATLAB Computations with Sparse and Factored Tensors
SIAM Journal on Scientific Computing
A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor
IEEE Transactions on Signal Processing
Tensor Decompositions and Applications
SIAM Review
MultiAspectForensics: Pattern Mining on Large-Scale Heterogeneous Networks with Tensor Analysis
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Blind PARAFAC receivers for DS-CDMA systems
IEEE Transactions on Signal Processing
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How can we efficiently decompose a tensor into sparse factors, when the data does not fit in memory? Tensor decompositions have gained a steadily increasing popularity in data mining applications, however the current state-of-art decomposition algorithms operate on main memory and do not scale to truly large datasets. In this work, we propose ParCube, a new and highly parallelizable method for speeding up tensor decompositions that is well-suited to producing sparse approximations. Experiments with even moderately large data indicate over 90% sparser outputs and 14 times faster execution, with approximation error close to the current state of the art irrespective of computation and memory requirements. We provide theoretical guarantees for the algorithm's correctness and we experimentally validate our claims through extensive experiments, including four different real world datasets (Enron, Lbnl, Facebook and Nell), demonstrating its effectiveness for data mining practitioners. In particular, we are the first to analyze the very large Nell dataset using a sparse tensor decomposition, demonstrating that ParCube enables us to handle effectively and efficiently very large datasets.