{
  "id": "1509.05647",
  "version": "v1",
  "published": "2015-09-18T15:03:03.000Z",
  "updated": "2015-09-18T15:03:03.000Z",
  "title": "Fast and Simple PCA via Convex Optimization",
  "authors": [
    "Dan Garber",
    "Elad Hazan"
  ],
  "categories": [
    "math.OC",
    "cs.LG",
    "cs.NA",
    "math.NA"
  ],
  "abstract": "The problem of principle component analysis (PCA) is traditionally solved by spectral or algebraic methods. We show how PCA could be formulated as a sequence of {\\it convex} optimization problems. This gives rise to a new efficient method for computing the PCA based on recent advances in stochastic methods for convex optimization. In particular, we present running times that improve over the current state-of-the-art.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-09-18T15:03:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convex optimization",
      "simple pca",
      "principle component analysis",
      "algebraic methods",
      "optimization problems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015arXiv150905647G"
    }
  }
}