Scaling Locally Linear Embedding

Summary: Ripple scales Locally Linear Embedding by incrementally updating edge weights via the Woodbury formula and computing kernel eigenvectors via an LU-based inverse power method. It preserves identical dimensionality reductions while delivering substantial speedups over vanilla LLE for large-scale data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5397
Venue: SIGMOD
Year: 2017
Pagerank: 4.1945683e-05
Overall Rank: 11,787 | 18.00%
DOI: 10.1145/3035918.3064021

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 10 of 10 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
65	Fast Subsequence Matching in Time-Series Databases	1994	SIGMOD	0.00062029383
243	Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases	2001	SIGMOD	0.00031074984
693	Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences	1997	SIGMOD	0.00018077335
1,363	Indexing Spatio-Temporal Trajectories with Chebyshev Polynomials	2004	SIGMOD	0.00012372959
1,641	Fast and Exact Top-k Search for Random Walk with Restart	2012	VLDB	0.00011047924
2,041	Indexable PLA for Efficient Similarity Search	2007	VLDB	9.6992894e-05
2,898	SCAN++: Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs	2015	VLDB	7.9445129e-05
6,578	Madeus: Database Live Migration Middleware under Heavy Workloads for Cloud Environment	2015	SIGMOD	5.0037147e-05
11,955	Scaling Manifold Ranking Based Image Retrieval	2015	VLDB	4.1945683e-05
13,337	Fast Algorithm for the Lasso based L1-Graph Construction	2017	VLDB	-

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