Multi-modal Differentiable Unsupervised Feature Selection
- URL: http://arxiv.org/abs/2303.09381v1
- Date: Thu, 16 Mar 2023 15:11:17 GMT
- Title: Multi-modal Differentiable Unsupervised Feature Selection
- Authors: Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe
- Abstract summary: In multi-modal measurements, many observed variables in both modalities are often nuisance and do not carry information about the phenomenon of interest.
Here, we propose a multi-modal unsupervised feature selection framework: identifying informative variables based on coupled high-dimensional measurements.
We incorporate the scores with differentiable gates that mask nuisance features and enhance the accuracy of the structure captured by the graph Laplacian.
- Score: 5.314466196448187
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-modal high throughput biological data presents a great scientific
opportunity and a significant computational challenge. In multi-modal
measurements, every sample is observed simultaneously by two or more sets of
sensors. In such settings, many observed variables in both modalities are often
nuisance and do not carry information about the phenomenon of interest. Here,
we propose a multi-modal unsupervised feature selection framework: identifying
informative variables based on coupled high-dimensional measurements. Our
method is designed to identify features associated with two types of latent
low-dimensional structures: (i) shared structures that govern the observations
in both modalities and (ii) differential structures that appear in only one
modality. To that end, we propose two Laplacian-based scoring operators. We
incorporate the scores with differentiable gates that mask nuisance features
and enhance the accuracy of the structure captured by the graph Laplacian. The
performance of the new scheme is illustrated using synthetic and real datasets,
including an extended biological application to single-cell multi-omics.
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