Summary of Robust Learning Under Hybrid Noise, by Yang Wei et al.
Robust Learning under Hybrid Noise
by Yang Wei, Shuo Chen, Shanshan Ye, Bo Han, Chen Gong
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 paper proposes a novel unified learning framework, called “Feature and Label Recovery” (FLR), to combat hybrid noise in machine learning models. Hybrid noise combines both feature noise and label noise, which is common in real-world applications due to unreliable data collection and annotation processes. FLR concurrently reconstructs the feature matrix and label matrix of input data using low-rank approximation and nuclear norm regularization. The framework leads to a non-convex optimization problem, solved by a non-convex Alternating Direction Method of Multipliers (ADMM) with convergence guarantee. Experimental results show the superiority of FLR over state-of-the-art robust learning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in machine learning: when we have noisy data. Noisy data means that some information is incorrect or missing, which makes it hard for machines to learn from it. The authors propose a new way to fix this problem by combining two techniques: one for fixing the noise in features (like images) and another for fixing the noise in labels (what the machine is supposed to learn). This method works well on real-world datasets and outperforms other methods. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regularization
