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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
11313
Venue
VLDB
Year
2016
Pagerank
6.1191532e-05
Overall Rank
5,877 | 59.16%
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
152 LOF: Identifying Density-Based Local Outliers 2000 SIGMOD 0.00029288871
1,231 Streaming Pattern Discovery in Multiple Time-Series 2005 VLDB 0.00011658901
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