Summary of Personalized Clustering Via Targeted Representation Learning, by Xiwen Geng et al.
Personalized Clustering via Targeted Representation Learning
by Xiwen Geng, Suyun Zhao, Yixin Yu, Borui Peng, Pan Du, Hong Chen, Cuiping Li, Mengdie Wang
First submitted to arxiv on: 18 Dec 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 proposed personalized clustering method utilizes user input in the form of modicum task information (e.g., must-link or cannot-link pairs) to guide the clustering direction. This is achieved by querying users with the most informative pairs, facilitating representation learning based on clustering preferences. The targeted representation is learned and augmented through attention mechanisms and constrained contrastive loss. This method ensures that the risk of personalized clustering is tightly bounded, making it effective even when only a limited number of queries are available. Experimental results demonstrate its performance across various clustering tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to group data called personalized clustering. It asks users for help by showing them pairs of things that should be together or apart. This helps the computer learn how to group things in a way that makes sense to people. The method uses attention mechanisms and special losses to make sure it gets better with more user input. This is important because it means we can get good results even when we don’t have too much information from users. |
Keywords
» Artificial intelligence » Attention » Clustering » Contrastive loss » Representation learning
