Multi-aspect-streaming tensor analysis
作者：
Highlights：
• We extend the application of histograms to tensor analysis problem.
• We propose the first approach for multi-aspect-streaming tensor analysis (MASTA).
• MASTA is space-efficient, fast and constant-time for update.
• We evaluate the strengths and weaknesses of MASTA on 11 real-life data sets.
• The proposed approach is useful for both streaming and scalable problems.
摘要
•We extend the application of histograms to tensor analysis problem.•We propose the first approach for multi-aspect-streaming tensor analysis (MASTA).•MASTA is space-efficient, fast and constant-time for update.•We evaluate the strengths and weaknesses of MASTA on 11 real-life data sets.•The proposed approach is useful for both streaming and scalable problems.
论文关键词：Tensor analysis,Data streams,Online histogram,Tensor decomposition,Streaming tensor analysis
论文评审过程：Received 12 January 2015, Revised 14 July 2015, Accepted 15 July 2015, Available online 21 July 2015, Version of Record 19 October 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.07.013
