Designing Complex Experiments by Applying Unsupervised Machine Learning
- URL: http://arxiv.org/abs/2110.01458v2
- Date: Tue, 5 Oct 2021 21:35:36 GMT
- Title: Designing Complex Experiments by Applying Unsupervised Machine Learning
- Abstract summary: The article discusses the application of unsupervised machine learning to support the pragmatic designs of complex experiments.
A beta variational autoencoder (beta-VAE) has been applied to represent trials of the initial full factorial design.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Design of experiments (DOE) is playing an essential role in learning and
improving a variety of objects and processes. The article discusses the
application of unsupervised machine learning to support the pragmatic designs
of complex experiments. Complex experiments are characterized by having a large
number of factors, mixed-level designs, and may be subject to constraints that
eliminate some unfeasible trials for various reasons. Having such attributes,
it is very challenging to design pragmatic experiments that are economically,
operationally, and timely sound. It means a significant decrease in the number
of required trials from a full factorial design, while still attempting to
achieve the defined objectives. A beta variational autoencoder (beta-VAE) has
been applied to represent trials of the initial full factorial design after
filtering out unfeasible trials on the low dimensional latent space. Regarding
visualization and interpretability, the paper is limited to 2D representations.
Beta-VAE supports (1) orthogonality of the latent space dimensions, (2)
isotropic multivariate standard normal distribution of the representation on
the latent space, (3) disentanglement of the latent space representation by
levels of factors, (4) propagation of the applied constraints of the initial
design into the latent space, and (5) generation of trials by decoding latent
space points. Having an initial design representation on the latent space with
such properties, it allows for the generation of pragmatic design of
experiments (G-DOE) by specifying the number of trials and their pattern on the
latent space, such as square or polar grids. Clustering and aggregated gradient
metrics have been shown to guide grid specification.
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