Summary of Linear Optimal Transport Subspaces For Point Set Classification, by Mohammad Shifat E Rabbi et al.
Linear optimal transport subspaces for point set classification
by Mohammad Shifat E Rabbi, Naqib Sad Pathan, Shiying Li, Yan Zhuang, Abu Hasnat Mohammad Rubaiyat, Gustavo K Rohde
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed framework for classifying point sets experiencing spatial deformations employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data. This approach demonstrates label efficiency, non-iterative behavior, and requires no hyper-parameter tuning. It achieves competitive accuracies compared to state-of-the-art methods across various point set classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to classify groups of points that have been changed by certain types of spatial transformations. The method uses the Linear Optimal Transport (LOT) transform to turn these sets into a linear space, making it easier to recognize and classify them. This approach is faster and requires less tuning than existing methods, and performs well on several different classification tasks. |
Keywords
* Artificial intelligence * Classification * Embedding
