Summary of Geodesic Flow Kernels For Semi-supervised Learning on Mixed-variable Tabular Dataset, by Yoontae Hwang et al.
Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset
by Yoontae Hwang, Yongjae Lee
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes GFTab, a semi-supervised framework for learning on tabular data. Tabular data combines continuous and categorical variables, posing unique challenges for machine learning algorithms. GFTab incorporates three key innovations: variable-specific corruption methods, Geodesic flow kernel similarity measures, and tree-based embeddings. The framework is evaluated on 21 datasets from various domains, sizes, and compositions. Results show that GFTab outperforms existing models in many cases, especially when labeled data is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GFTab is a new way to learn from tabular data, which is important because this type of data has both numbers and categories. The approach uses three special techniques: one for numbers, one for categories, and one that connects them together. To test GFTab, the researchers collected 21 datasets with different types of data and sizes. They found that GFTab worked well on many of these datasets, especially when there wasn’t much labeled data. |
Keywords
» Artificial intelligence » Machine learning » Semi supervised
