mlsanityData to 14 Oct 2026

A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction

arXiv 2202.02904 ↗First posted 7 Feb 2022cs.LG cs.NA math.NAcited by 4

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Abstract (arXiv)

A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one in citeLaiMckenzie2020. However, under a weaker assumption and with less computational complexity than the one in citeLaiMckenzie2020, the algorithm is shown to be able to find a desired cluster with high probability. The ``one cluster at a time" feature of our method distinguishes it from other global clustering methods. Several numerical experiments are conducted on the synthetic data such as stochastic block model and real data such as MNIST, political blogs network, AT&T and YaleB human faces data sets to demonstrate the effectiveness and efficiency of our algorithm.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 53 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

12 of 53 references.

References · 12

  1. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
  2. 2017Community Detection and Stochastic Block Modelscited by 319
  3. 2015Semi-Supervised Learning with Ladder Networkscited by 247
  4. 2019Deep Compressed Sensingcited by 46
  5. 2013Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodelcited by 45
  6. 2013A Proof Of The Block Model Threshold Conjecturecited by 36
  7. 2015Achieving Optimal Misclassification Proportion in Stochastic Block Modelcited by 35
  8. 2014Heat kernel based community detectioncited by 30
  9. 2015Recovering communities in the general stochastic block model without knowing the parameterscited by 13
  10. 2017Capacity Releasing Diffusion for Speed and Localitycited by 10
  11. 2018Flow-Based Local Graph Clustering with Better Seed Set Inclusioncited by 8
  12. 2019Statistical guarantees for local graph clusteringcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2022Graph-based Semi-supervised Local Clustering with Few Labeled Nodescited by 3
  2. 2024Local Clustering for Lung Cancer Image Classification via Sparse Solution Techniquecited by 1
  3. 2025Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methodscited by 1
  4. 2026Scalable Graph Coreset Selection via Greedy Samplingcited by 0