Multiblock-Networks: A Neural Network Analog to Component Based Methods
for Multi-Source Data
- URL: http://arxiv.org/abs/2109.10279v1
- Date: Tue, 21 Sep 2021 16:00:15 GMT
- Title: Multiblock-Networks: A Neural Network Analog to Component Based Methods
for Multi-Source Data
- Authors: Anna Jenul and Stefan Schrunner and Runar Helin and Kristian Hovde
Liland and Cecilia Marie Futs{\ae}ther and Oliver Tomic
- Abstract summary: We propose a setup to transfer the concepts of component based statistical models to neural network architectures.
Thereby, we combine the flexibility of neural networks with the concepts for interpreting block relevance in multiblock methods.
Our results underline that multiblock networks allow for basic model interpretation while matching the performance of ordinary feed-forward neural networks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Training predictive models on datasets from multiple sources is a common, yet
challenging setup in applied machine learning. Even though model interpretation
has attracted more attention in recent years, many modeling approaches still
focus mainly on performance. To further improve the interpretability of machine
learning models, we suggest the adoption of concepts and tools from the
well-established framework of component based multiblock analysis, also known
as chemometrics. Nevertheless, artificial neural networks provide greater
flexibility in model architecture and thus, often deliver superior predictive
performance. In this study, we propose a setup to transfer the concepts of
component based statistical models, including multiblock variants of principal
component regression and partial least squares regression, to neural network
architectures. Thereby, we combine the flexibility of neural networks with the
concepts for interpreting block relevance in multiblock methods. In two use
cases we demonstrate how the concept can be implemented in practice, and
compare it to both common feed-forward neural networks without blocks, as well
as statistical component based multiblock methods. Our results underline that
multiblock networks allow for basic model interpretation while matching the
performance of ordinary feed-forward neural networks.
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