Summary of Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (rsic), by Marc A. Tunnell and Zachary J. Debruine and Erin Carrier
Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)
by Marc A. Tunnell, Zachary J. DeBruine, Erin Carrier
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Determining the optimal rank in Non-negative Matrix Factorization (NMF) is crucial, but traditional methods often require extensive parameter tuning and domain-specific knowledge. Our novel approach, Residual Sensitivity to Intial Conditions (RSIC), suggests multiple ranks of interest by analyzing the sensitivity of relative residuals to different initializations. By computing Mean Coordinatewise Interquartile Range (MCI) of residuals across multiple random initializations, RSIC identifies regions where NMF solutions are less sensitive to initial conditions and potentially more meaningful. We evaluated RSIC on diverse datasets, including single-cell gene expression data, image data, and text data, comparing it against state-of-the-art existing rank determination methods. Our experiments demonstrate that RSIC effectively identifies relevant ranks consistent with the underlying structure of the data, outperforming traditional methods in computationally infeasible or less accurate scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to use a machine learning tool called Non-negative Matrix Factorization (NMF). Right now, figuring out how many “factors” (or pieces) to break down data into requires a lot of work and special knowledge. The researchers created a new method called RSIC that can help find the right number of factors by looking at how sensitive different starting points are. They tested this method on lots of different types of data, like pictures and words, and it worked better than old methods in many cases. |
Keywords
* Artificial intelligence * Machine learning
