{
  "id": "1701.03268",
  "version": "v1",
  "published": "2017-01-12T08:48:03.000Z",
  "updated": "2017-01-12T08:48:03.000Z",
  "title": "Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models",
  "authors": [
    "Hideyuki Miyahara",
    "Koji Tsumura",
    "Yuki Sughiyama"
  ],
  "comment": "Presented at IEEE CDC 2016, the 2016 IEEE 55th Conference on Decision and Control (CDC)",
  "doi": "10.1109/CDC.2016.7798981",
  "categories": [
    "stat.ML",
    "cond-mat.stat-mech",
    "quant-ph"
  ],
  "abstract": "We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM) algorithm is an established algorithm to compute maximum likelihood estimates and applied to many practical applications. However, it is known that EM heavily depends on initial values and its estimates are sometimes trapped by local optima. To solve such a problem, quantum annealing (QA) was proposed as a novel optimization approach motivated by quantum mechanics. By employing QA, we then formulate DQAEM and present a theorem that supports its stability. Finally, we demonstrate numerical simulations to confirm its efficiency.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-01-12T08:48:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "gaussian mixture models",
      "em algorithm",
      "relaxation",
      "deterministic quantum annealing expectation-maximization",
      "novel optimization approach"
    ],
    "tags": [
      "conference paper",
      "journal article"
    ],
    "publication": {
      "publisher": "IEEE"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}