{
  "id": "2211.11114",
  "version": "v1",
  "published": "2022-11-20T22:55:07.000Z",
  "updated": "2022-11-20T22:55:07.000Z",
  "title": "Semi-supervised Local Cluster Extraction by Compressive Sensing",
  "authors": [
    "Zhaiming Shen",
    "Ming-Jun Lai",
    "Sheng Li"
  ],
  "categories": [
    "cs.LG",
    "cs.NA",
    "math.NA"
  ],
  "abstract": "Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we propose a new semi-supervised local cluster extraction approach by applying the idea of compressive sensing based on two pioneering works under the same framework. Our approves improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of existing works, which is the low quality of initial cut. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our approach.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-11-20T22:55:07.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "compressive sensing",
      "small local structure inside",
      "multiple benchmark datasets demonstrate",
      "initial cut",
      "semi-supervised local cluster extraction approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}