تقرير
Visualizing probabilistic models: Intensive Principal Component Analysis
| العنوان: | Visualizing probabilistic models: Intensive Principal Component Analysis |
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| المؤلفون: | Quinn, Katherine N., Clement, Colin B., De Bernardis, Francesco, Niemack, Michael D., Sethna, James P. |
| سنة النشر: | 2018 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Statistical Mechanics, Physics - Data Analysis, Statistics and Probability |
| الوصف: | Unsupervised learning makes manifest the underlying structure of data without curated training and specific problem definitions. However, the inference of relationships between data points is frustrated by the `curse of dimensionality' in high-dimensions. Inspired by replica theory from statistical mechanics, we consider replicas of the system to tune the dimensionality and take the limit as the number of replicas goes to zero. The result is the intensive embedding, which is not only isometric (preserving local distances) but allows global structure to be more transparently visualized. We develop the Intensive Principal Component Analysis (InPCA) and demonstrate clear improvements in visualizations of the Ising model of magnetic spins, a neural network, and the dark energy cold dark matter ({\Lambda}CDM) model as applied to the Cosmic Microwave Background. Comment: 6 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1073/pnas.1817218116 |
| URL الوصول: | http://arxiv.org/abs/1810.02877 |
| رقم الأكسشن: | edsarx.1810.02877 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1073/pnas.1817218116 |
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