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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
5459
Venue
SIGMOD
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,992 | 17.73%
DOI
10.1145/3035918.3064021

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BibTeX Citation

@inproceedings{fujiwara_sigmod17,
        title = {{Scaling Locally Linear Embedding}},
        author = {Fujiwara, Yasuhiro and Marumo, Naoki and Blondel, Mathieu and Takeuchi, Koh and Kim, Hideaki and Iwata, Tomoharu and Ueda, Naonori},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064021},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064021},
        year = {2017}
}

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