LEARNING GRADIENTS FROM NONIDENTICAL DATA

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 infor...

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Bibliographic Details
Published in:The ANZIAM journal 2017-04, Vol.58 (3-4), p.220-230
Main Author: DONG, XUE-MEI
Format: Article
Language:English
Subjects:
Algorithms
Feature selection
Random variables
Sparsity
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Summary: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.
ISSN:1446-1811
1446-8735
DOI:10.1017/S1446181116000328