{
  "id": "2101.12416",
  "version": "v1",
  "published": "2021-01-29T06:06:58.000Z",
  "updated": "2021-01-29T06:06:58.000Z",
  "title": "Covariance Prediction via Convex Optimization",
  "authors": [
    "Shane Barratt",
    "Stephen Boyd"
  ],
  "categories": [
    "stat.ML",
    "cs.AI",
    "cs.LG",
    "math.OC"
  ],
  "abstract": "We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. We describe a covariance predictor that has the form of a generalized linear model, i.e., an affine function of the features followed by an inverse link function that maps vectors to symmetric positive definite matrices. The log-likelihood is a concave function of the predictor parameters, so fitting the predictor involves convex optimization. Such predictors can be combined with others, or recursively applied to improve performance.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-01-29T06:06:58.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "convex optimization",
      "covariance prediction",
      "zero mean gaussian vector",
      "symmetric positive definite matrices",
      "inverse link function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}