Summary of One-bit Quantization and Sparsification For Multiclass Linear Classification with Strong Regularization, by Reza Ghane et al.
One-Bit Quantization and Sparsification for Multiclass Linear Classification with Strong Regularization
by Reza Ghane, Danil Akhtiamov, Babak Hassibi
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper investigates the application of linear regression for multiclass classification in scenarios where some training data is mislabeled. To prevent overfitting these mislabeled data, an explicit regularization term is introduced, which is proportional to a convex function of the model’s weights. The study assumes that the data comes from a Gaussian Mixture Model with equal class sizes and a proportion of corrupted labels for each class. Under these conditions, it is shown that the best classification performance is achieved when using a squared-L2 regularization term and infinite lambda values. Furthermore, the paper analyzes the classification errors for L1 and L∞ regularization terms in the large lambda regime, revealing the possibility to find sparse and one-bit solutions that perform similarly to the optimal solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to use linear regression for multi-class classification when some of the training data is wrong. To avoid learning these mistakes, a special penalty term is added to the model’s weights. The researchers assume the data comes from a mix of normal distributions with equal class sizes and that some labels are incorrect. They find that using a specific type of regularization helps get the best results. Then, they look at other types of regularization and show how sparse and simple solutions can work almost as well. |
Keywords
* Artificial intelligence * Classification * Linear regression * Mixture model * Overfitting * Regularization
