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Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space
Author(s)
Date Issued
2017-12-30
Date Available
2017-11-28T12:52:34Z
Abstract
We present a new approach for learning a sequence regression function, i.e., a mapping from sequential observations to a numeric score. Our learning algorithm employs coordinate gradient descent with Gauss-Southwell optimization in the feature space of all subsequences. We give a tight upper bound for the coordinate wise gradients of squared error loss which enables efficient Gauss-Southwell selection. The proposed bound is built by separating the positive and the negative gradients of the loss function and exploits the structure of the feature space. Extensive experiments on simulated as well as real-world sequence regression benchmarks show that the bound is effective and our proposed learning algorithm is efficient and accurate. The resulting linear regression model provides the user with a list of the most predictive features selected during the learning stage, adding to the interpretability of the method. Code and data related to this chapter are available at: https://github.com/svgsponer/SqLoss.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
Springer
Series
Lecture Notes in Computer Science
Copyright (Published Version)
2017 Springer
Keywords
Language
English
Status of Item
Peer reviewed
Part of
Ceci M., Hollmén J., Todorovski L., Vens C., Džeroski S. (eds). Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol 10535
Conference Details
The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, Skopje, Macedonia 18-22 September 2017
ISBN
978-3-319-71245-1
This item is made available under a Creative Commons License
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