Summary of Grvfl-mv: Graph Random Vector Functional Link Based on Multi-view Learning, by M. Tanveer et al.
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning
by M. Tanveer, R. K. Sharma, M. Sajid, A. Quadir
First submitted to arxiv on: 7 Sep 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 The novel graph random vector functional link based on multi-view learning (GRVFL-MV) model is proposed to address the limitations of the traditional random vector functional link (RVFL). The GRVFL-MV model leverages multiview learning and graph embedding frameworks, enabling efficient learning, comprehensive representation, and structural awareness. By fusing information from diverse perspectives, the model can capture complex patterns and relationships within data, improving generalization performance. Evaluation on various datasets demonstrates the GRVFL-MV’s superior performance compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new machine learning model called GRVFL-MV that helps computers learn better by combining information from different views of data. This is useful because sometimes we have multiple ways of looking at the same data, and combining them can give us a more complete picture. The model uses two techniques to help it learn: one that looks at relationships between different parts of the data, and another that helps it understand how the different views fit together. The paper shows that this new model performs better than older models on many different types of data. |
Keywords
» Artificial intelligence » Embedding » Generalization » Machine learning
