Summary of Generalizable and Robust Spectral Method For Multi-view Representation Learning, by Amitai Yacobi et al.
Generalizable and Robust Spectral Method for Multi-view Representation Learning
by Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Multi-view representation learning (MvRL) has gained significant attention due to increasing demands for processing and analyzing multi-source data. Graph Laplacian-based MvRL methods have shown remarkable success but struggle with generalization to new data, scalability, and noisy or outlier-contaminated data. To overcome these challenges, we introduce SpecRaGE, a novel fusion-based framework that integrates graph Laplacian and deep learning methods. SpecRaGE uses neural networks to learn parametric mapping approximating joint diagonalization of graph Laplacians, bypassing alignment requirements while enabling generalizable and scalable learning. A meta-learning fusion module dynamically adapts to data quality, ensuring robustness against outliers and noisy views. Our experiments demonstrate that SpecRaGE outperforms state-of-the-art methods, particularly in scenarios with contaminated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-view representation learning helps computers understand data from different sources. Right now, there are some ways to do this, but they have limitations. They can’t handle new data well and struggle when the data is noisy or has outliers. We created a new way called SpecRaGE that combines two approaches: graph Laplacian and deep learning. This helps computers learn better and faster, even with messy data. Our tests show that SpecRaGe does better than other methods in these situations. |
Keywords
» Artificial intelligence » Alignment » Attention » Deep learning » Generalization » Meta learning » Representation learning
