Covariance Prediction via Convex Optimization

Shane Barratt; Stephen Boyd

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arXiv · 2101.12416

Covariance Prediction via Convex Optimization

Shane Barratt·Stephen Boyd

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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.

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BibTeX RIS

Shane Barratt, Stephen Boyd. 2021-01-29. Covariance Prediction via Convex Optimization. https://arxiv.org/abs/2101.12416

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