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Streaming Anomaly Detection Using Randomized Matrix Sketching

Summary: Unsupervised, one-pass streaming anomaly detection via randomized matrix sketching; maintains an orthogonal basis for observed data. Anomalies detected by reconstruction error; competitive with offline SVD, with speedups from randomized low-rank approximations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11312
Venue
VLDB
Year
2016
Pagerank
5.244512e-05
Overall Rank
5,984 | 58.38%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
161 LOF: Identifying Density-Based Local Outliers 2000 SIGMOD 0.00039846974
1,346 Streaming Pattern Discovery in Multiple Time-Series 2005 VLDB 0.00012466288
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