Sparse Modality Regression

MCML Authors

Chris Kolb

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Christian Müller

Prof. Dr.

Principal Investigator

Biomedical Statistics and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

Deep neural networks (DNNs) enable learning from various data modalities, such as images or text. This concept has also found its way into statistical modelling through the use of semi-structured regression, a model additively combining structured predictors with unstructured effects from arbitrary data modalities learned through a DNN. This paper introduces a new framework called sparse modality regression (SMR). SMR is a regression model combining different data modalities and uses a group lasso-type regularization approach to perform modality selection by zeroing out potentially uninformative modalities.

inproceedings

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IWSM 2023

37th International Workshop on Statistical Modelling. Dortmund, Germany, Jul 17-21, 2023. Best Paper Award.

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Authors

C. Kolb • B. Bischl • C. L. Müller • D. Rügamer

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Links

PDF

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Research Areas

 A1 | Statistical Foundations & Explainability

 C2 | Biology