Summary of Deep Online Probability Aggregation Clustering, by Yuxuan Yan et al.
Deep Online Probability Aggregation Clustering
by Yuxuan Yan, Na Lu, Ruofan Yan
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to deep clustering, called Probability Aggregation Clustering (PAC), which combines machine learning with deep models to achieve remarkable superiority. The method modifies the data processing pipeline into two alternating phases: feature clustering and model training. However, this may lead to instability and computational burden issues. To address these concerns, the authors propose a centerless clustering algorithm that proactively adapts deep learning technologies, enabling easy deployment in online deep clustering. PAC circumvents the cluster center and aligns the probability space and distribution space by formulating clustering as an optimization problem with a novel objective function. The method is evaluated on various datasets and outperforms state-of-the-art deep clustering methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group things together, called Probability Aggregation Clustering (PAC), which combines two different approaches: machine learning and deep learning. The goal is to make this process easier and faster by changing the order in which it’s done. This helps solve some problems that happened before, like making it too hard or taking too long. PAC makes sure the groupings are accurate and consistent by looking at probability and distribution spaces in a new way. The results show that this method is better than what others have tried. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Machine learning » Objective function » Optimization » Probability
