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LEARNING GRADIENTS FROM NONIDENTICAL DATA

Part of: Linear inference, regression Artificial intelligence (68Txx)

Published online by Cambridge University Press:  07 March 2017

XUE-MEI DONG Open the ORCID record for XUE-MEI DONG [Opens in a new window]
XUE-MEI DONG*
Affiliation:
School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China email dongxuemei@zjgsu.edu.cn
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Abstract

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Selecting important variables and estimating coordinate covariation have received considerable attention in the current big data deluge. Previous work shows that the gradient of the regression function, the objective function in regression and classification problems, can provide both types of information. In this paper, an algorithm to learn this gradient function is proposed for nonidentical data. Under some mild assumptions on data distribution and the model parameters, a result on its learning rate is established which provides a theoretical guarantee for using this method in dynamical gene selection and in network security for recognition of malicious online attacks.

Keywords

gradient learningvariable selectioncoordinate covariation estimation

MSC classification

Primary: 68T05: Learning and adaptive systems
Secondary: 62J02: General nonlinear regression
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 220 - 230
Copyright
© 2017 Australian Mathematical Society 

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