2014 LWISVDLowRankWindowedIncrementa

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Abstract

Singular Value Decomposition (SVD) is computationally costly and therefore a naive implementation does not scale to the needs of scenarios where data evolves continuously. While there are various on-line analysis and incremental decomposition techniques, these may not accurately represent the data or may be slow for the needs of many applications. To address these challenges, in this paper, we propose a Low-rank, Windowed, Incremental SVD (LWI-SVD) algorithm, which (a) leverages efficient and accurate low-rank approximations to speed up incremental SVD updates and (b) uses a window-based approach to aggregate multiple incoming updates (insertions or deletions of rows and columns) and, thus, reduces on - line processing costs. We also present an LWI-SVD with restarts (LWI2-SVD) algorithm which leverages a novel highly efficient partial reconstruction based change detection scheme to support timely refreshing of the decomposition with significant changes in the data and prevent accumulation of errors over time. Experiment results, including comparisons to other state of the art techniques on different data sets and under different parameter settings, confirm that LWI-SVD and LWI2-SVD are both efficient and accurate in maintaining decompositions.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 LWISVDLowRankWindowedIncrementaXilun Chen
K. Selcuk Candan
LWI-SVD: Low-rank, Windowed, Incremental Singular Value Decompositions on Time-evolving Data Sets10.1145/2623330.26236712014
AuthorXilun Chen + and K. Selcuk Candan +
conferenceKDD-2014 +
doi10.1145/2623330.2623671 +
proceedingsProceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +
titleLWI-SVD: Low-rank, Windowed, Incremental Singular Value Decompositions on Time-evolving Data Sets +
year2014 +