Summary of Limited Memory Online Gradient Descent For Kernelized Pairwise Learning with Dynamic Averaging, by Hilal Alquabeh et al.
Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averaging
by Hilal AlQuabeh, William de Vazelhes, Bin Gu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces a new algorithm for pairwise learning in machine learning, which addresses loss functions defined on pairs of training examples. The existing OGD method is improved by generalizing to kernel pairwise learning and removing the independence assumption. The new algorithm uses a random example and a moving average to build the gradient, resulting in a sub-linear regret bound with complexity O(T). The paper also integrates random Fourier features to minimize the complexity of kernel calculations. Experiments on real-world datasets show that the proposed technique outperforms kernel and linear algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning faster and more efficient when working with pairs of examples. Right now, this kind of learning can be slow as the number of examples grows. Researchers have found a way to make it faster using an algorithm called online gradient descent (OGD). But there’s still room for improvement. The new algorithm introduced in this paper makes OGD work better and also works with more complex models that involve kernels. This means it can handle bigger datasets and do better on certain tasks. The results are promising, showing the new algorithm performs well on real-world data. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning
