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2006 JMLR JMLR 2006

Estimation of Gradients and Coordinate Covariation in Classification

Abstract

We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is to find salient variables and estimate how they covary. An efficient implementation with respect to both memory and time is given. The utility of the algorithm is illustrated on simulated data as well as a gene expression data set. An error analysis is given for the convergence of the estimate of the classification function and its gradient to the true classification function and true gradient. [abs] [ pdf ][ bib ] © JMLR 2006. (edit, beta)

🧭 Keyword Pioneer — classification function
🐣 Hot Topic Early Bird — feature 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 , Qiang Wu

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Learning Types > Supervised Learning

Keywords

feature learning variable selection feature selection supervised learning gradient estimation classification function gene expression datum covariation estimation
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