{
  "id": "1706.09693",
  "version": "v1",
  "published": "2017-06-29T11:45:54.000Z",
  "updated": "2017-06-29T11:45:54.000Z",
  "title": "Image classification using local tensor singular value decompositions",
  "authors": [
    "Elizabeth Newman",
    "Misha Kilmer",
    "Lior Horesh"
  ],
  "comment": "Submitted to IEEE CAMSAP 2017 Conference, 5 pages, 9 figures and tables",
  "categories": [
    "stat.ML",
    "cs.LG",
    "stat.CO"
  ],
  "abstract": "From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers. However, the most accurate classifiers typically have significantly high storage costs, or require complicated procedures that may be computationally expensive. We present a novel (nonlinear) classification approach using truncation of local tensor singular value decompositions (tSVD) that robustly offers accurate results, while maintaining manageable storage costs. Our approach takes advantage of the optimality of the representation under the tensor algebra described to determine to which class an image belongs. We extend our approach to a method that can determine specific pairwise match scores, which could be useful in, for example, object recognition problems where pose/position are different. We demonstrate the promise of our new techniques on the MNIST data set.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-06-29T11:45:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "local tensor singular value decompositions",
      "image classification",
      "determine specific pairwise match scores",
      "storage costs",
      "object recognition problems"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}