Summary of Fast and Interpretable Support Vector Classification Based on the Truncated Anova Decomposition, by Kseniya Akhalaya et al.
Fast and interpretable Support Vector Classification based on the truncated ANOVA decomposition
by Kseniya Akhalaya, Franziska Nestler, Daniel Potts
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Machine Learning (stat.ML)
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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 method uses Support Vector Machines (SVMs) to solve classification problems in high-dimensional spaces. It employs feature maps based on trigonometric functions or wavelets, which are more efficient than classical Fast Fourier Transform (FFT)-based methods for large dimensions. The approach is motivated by the sparsity of effects and recent results regarding function reconstruction from scattered data using truncated analysis of variance (ANOVA) decompositions. To enforce sparsity in basis coefficients, the method uses both _2-norm and _1-norm regularization. Numerical experiments demonstrate the ability to recover the signum of a fitting function and achieve better results with _1-norm regularization on various artificial and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method for solving Support Vector Machines (SVMs) in high-dimensional spaces. It uses special functions called trigonometric or wavelet features to make the problem easier to solve. The approach is based on ideas from statistics, which help explain how the features are related. To make the solution more efficient, the method only looks at a small number of these features at a time. This makes it possible to use the method even when there is a lot of data. The results show that this new method can recover the underlying function and perform better than other methods on some datasets. |
Keywords
* Artificial intelligence * Classification * Regularization
