Summary of Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis, by Jiayu Su et al.
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis
by Jiayu Su, David A. Knowles, Raul Rabadan
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Genomics (q-bio.GN)
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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 introduces Supervised Independent Subspace Principal Component Analysis (sisPCA), a PCA extension designed for multi-subspace learning. The model leverages the Hilbert-Schmidt Independence Criterion (HSIC) to incorporate supervision and ensure subspace disentanglement. sisPCA connects with autoencoders and regularized linear regression, and is demonstrated to identify and separate hidden data structures through applications in breast cancer diagnosis, aging-associated DNA methylation changes, and single-cell analysis of malaria infection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better represent big amounts of data that are hard for humans to understand. The problem is that current methods can either do one thing well or many things poorly. This new method, called sisPCA, tries to solve this by learning multiple subspaces at the same time and making sure they don’t get mixed up. It’s shown to work well in several real-world applications, including finding hidden patterns in breast cancer images, understanding how people age, and studying how malaria infects cells. |
Keywords
» Artificial intelligence » Linear regression » Pca » Principal component analysis » Supervised
