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2006 JMLR JMLR 2006

Learning Coordinate Covariances via Gradients

Abstract

We introduce an algorithm that learns gradients from samples in the supervised learning framework. An error analysis is given for the convergence of the gradient estimated by the algorithm to the true gradient. The utility of the algorithm for the problem of variable selection as well as determining variable covariance is illustrated on simulated data as well as two gene expression data sets. For square loss we provide a very efficient implementation with respect to both memory and time. [abs] [ pdf ][ bib ] © JMLR 2006. (edit, beta)

🧭 Keyword Pioneer — covariance estimation
🐣 Hot Topic Early Bird — supervised learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Sayan Mukherjee , Ding-Xuan Zhou

Topics

Machine Learning > Core Methods > Regression Machine Learning > Core Methods > Representation Learning Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Core Methods > Feature Selection

Keywords

variable selection supervised learning covariance estimation gradient estimation gene expression gradient learning coordinate covariance
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