Summary of Exploring Space Efficiency in a Tree-based Linear Model For Extreme Multi-label Classification, by He-zhe Lin and Cheng-hung Liu and Chih-jen Lin
Exploring space efficiency in a tree-based linear model for extreme multi-label classification
by He-Zhe Lin, Cheng-Hung Liu, Chih-Jen Lin
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Extreme multi-label classification (XMC) deals with identifying relevant subsets from numerous labels. Tree-based linear models are effective due to their efficiency and simplicity in XMC. However, the space complexity of tree-based methods is not well-studied. This work investigates the space required to store a tree model under sparse data conditions, common in text data. The analysis reveals that unused features during training result in zero values in weight vectors, enabling storage of only non-zero elements for significant space savings. Experimental results show that tree models can achieve up to 95% reduction in storage space compared to the one-vs-rest method for multi-label text classification. This research provides a simple procedure for estimating the size of a tree model before training any classifier, potentially avoiding modifications through pruning or other techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer programs better at understanding lots of labels. It’s like trying to find all the different themes in a big book. The authors are looking at ways to make these programs more efficient and use less memory. They found that some parts of the program aren’t even used, so they can just store the important parts instead of everything. This can save up to 95% of the memory needed! The authors also developed a way to predict how much memory a program will need before it starts working. |
Keywords
» Artificial intelligence » Classification » Pruning » Text classification
