Summary of Sparse Domain Transfer Via Elastic Net Regularization, by Jingwei Zhang et al.
Sparse Domain Transfer via Elastic Net Regularization
by Jingwei Zhang, Farzan Farnia
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose Elastic Net Optimal Transport (ENOT) to address the problem of transporting samples across different domains in computer vision and language tasks while minimizing the modification of input features. The ENOT framework uses L1-norm and L2-norm regularization mechanisms to find a sparse and stable transportation map between the source and target domains. To compute the ENOT transport map, the researchers use the dual formulation of the ENOT optimization task and prove that the sparsified gradient of the optimal potential function in the ENOT’s dual representation provides the ENOT transport map. The paper demonstrates the application of ENOT to perform feature selection for sparse domain transfer and presents numerical results on several domain transfer problems using synthetic Gaussian mixtures and real image and text data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to move information from one place to another in computer vision and language tasks. They want to do this while making as few changes as possible. The new method uses two types of rules to find the best way to make these changes. It also helps identify which features are most important for moving information across different domains. |
Keywords
» Artificial intelligence » Feature selection » Optimization » Regularization
