Summary of Interpretable Label-free Self-guided Subspace Clustering, by Ivica Kopriva
Interpretable label-free self-guided subspace clustering
by Ivica Kopriva
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Majority subspace clustering (SC) algorithms often rely on one or more hyperparameters that require careful tuning to achieve high clustering performance. Traditional approaches involve using labeled data and grid-search, but this assumption doesn’t hold true in many domains, such as medicine. Researchers have explored developing SC algorithms free of hyperparameters, while others focus on label-independent hyperparameter optimization (HPO) tuning using internal clustering quality metrics. This paper proposes a novel approach to label-independent HPO that uses pseudo-labels and clustering quality metrics like accuracy (ACC) or normalized mutual information (NMI) to iteratively select subintervals of hyperparameters. By assuming ACC (or NMI) is a smooth function of hyperparameter values, the method can be applied to any SC algorithm. The proposed approach demonstrates comparable performance to oracle versions across six datasets representing digits, faces, and objects, with clustering performance typically 5-7% lower than that of the oracle versions. Additionally, the method’s interpretability is enhanced through visualizing subspace bases estimated from computed clustering partitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve majority subspace clustering (SC) algorithms without needing labeled data. Currently, SC algorithms rely on hyperparameters that need to be carefully chosen, but this can be difficult or even impossible in some cases, like medicine. The authors suggest a solution by using internal metrics from the SC algorithm itself to find the best hyperparameters. This approach uses something called pseudo-labels and accuracy (ACC) or normalized mutual information (NMI) to help make decisions. It’s a bit like trying different combinations of settings until you find one that works well. The method is tested on several SC algorithms and datasets, showing results that are close to the best possible outcome. |
Keywords
» Artificial intelligence » Clustering » Grid search » Hyperparameter » Optimization
