2012 | OriginalPaper | Buchkapitel
ParCube: Sparse Parallelizable Tensor Decompositions
verfasst von : Evangelos E. Papalexakis, Christos Faloutsos, Nicholas D. Sidiropoulos
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.