{
  "id": "1903.04042",
  "version": "v1",
  "published": "2019-03-10T18:56:14.000Z",
  "updated": "2019-03-10T18:56:14.000Z",
  "title": "Algorithms for an Efficient Tensor Biclustering",
  "authors": [
    "Andriantsiory Dina Faneva",
    "Mustapha Lebbah",
    "Hanane Azzag",
    "Gaël Beck"
  ],
  "comment": "Algorithms available on Clustering4Ever github, https://github.com/Clustering4Ever/Clustering4Ever",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Consider a data set collected by (individuals-features) pairs in different times. It can be represented as a tensor of three dimensions (Individuals, features and times). The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie in a low-dimensional subspace, modeling similarity among the signal trajectories while allowing different scalings across different individuals or different features. This approach are based on spectral decomposition in order to build the desired biclusters. We evaluate the quality of the results from each algorithms with both synthetic and real data set.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-10T18:56:14.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "efficient tensor biclustering",
      "algorithms",
      "signal trajectories",
      "real data set",
      "individuals"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}