Fast approximate spectral clustering

By Donghui Yan, Ling Huang, Michael I. Jordan. Knowledge Discovery and Data Mining, 2009

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/716ee519bc649236816f943f2b84b6ab58af76d0

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

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Citations in the corpus

Compressive Spectral Clustering (2016, methodology): “One can find similar methods in (Yan et al., 2009) and (Wang et al.”
Large Scale Spectral Clustering Via Landmark-Based Sparse Representation (2015, methodology): “1) KASP: k-means-based approximate spectral clustering method proposed in [38].”
Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, background): “Many works have been proposed to accelerate SC algorithm (Fowlkes et al. 2004), (Shinnou and Sasaki 2008), (Sakai and Imiya 2009), (Yan, Huang, and Jordan 2009), (Chen et al. 2011), (Chen and Cai 2011).”
Information theoretical estimators toolbox (2014, methodology): “Such methods are (i) robust and (ii) scale excellently, a single general desktop computer can handle about a million observations—in our case estimated ICA elements—within several minutes (Yan et al., 2009).”
K-means properties on six clustering benchmark datasets (2018, unknown): “Box 111, FIN-80101 Joensuu, Finland clustering [42].”
Random Forests for Big Data (2015, methodology): “One variant relies on subsampling while three others are related to parallel implementations of random forests and involve either various adaptations of bootstrap to Big Data or “divide-and-conquer” approaches.”
Unsupervised Semantic Segmentation by Distilling Feature Correspondences (2022, methodology): “More recent attempts to accelerate Spectral clustering such as (Yan et al., 2009) and (Han & Filippone, 2017) further assume a “Nonparametric” structure on the function φ , where a separate cluster assignment is learned for each vertex.”
Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation (2014, background): “Elsewhere, [30, 5] approximate the graph by representative points (aka landmarks).”
Large Scale Spectral Clustering with Landmark-Based Representation (2011, methodology): “Based on a similar idea, (Yan, Huang, and Jordan 2009) provided a general framework for fast approximate spectral clustering .”
A review of clustering techniques and developments (2017, background): “More literature (partially) on graph and spectral clustering can be seen in [182-190].”
Unsupervised Deep Embedding for Clustering Analysis (2015, methodology): “In order to scale spectral clustering to large datasets, approximate algorithms were invented to trade off performance for speed (Yan et al., 2009).”
Multi-View K-Means Clustering on Big Data (2013, unknown): “Although some research works have been proposed to to reduce the computational cost of the eigen-decomposition of the graph Laplacian [Yan et al., 2009] [Sakai and Imiya, 2009], they are designed for two-way clustering and have to use the hierarchical scheme to tackle the multi-way clustering…”
A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical (2013, background): “A thorough description of the perturbation analysis for the class of Gaussian data-adaptive weights is given in [47] and [78].”