Summary of Deep Feature Embedding For Tabular Data, by Yuqian Wu et al.
Deep Feature Embedding for Tabular Data
by Yuqian Wu, Hengyi Luo, Raymond S. T. Lee
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel deep embedding framework leverages lightweight deep neural networks to generate effective feature embeddings for tabular data in machine learning research. For numerical features, a two-step feature expansion and deep transformation technique captures copious semantic information. For categorical features, a unique identification vector for each entity is referred by a compact lookup table with a parameterized deep embedding function, transforming it into an embedding vector using a deep neural network. Experiments are conducted on real-world datasets for performance evaluation. The framework’s effectiveness is demonstrated through benchmarking against existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to turn tabular data into useful feature embeddings that machines can understand. It does this by using special kinds of deep neural networks to capture important information in numerical and categorical features. This method is tested on real-world datasets and shows promise for improving machine learning models. |
Keywords
» Artificial intelligence » Embedding » Machine learning » Neural network
