mancusolab/susiepca
Scalable Ultra-Sparse Bayesian PCA
When analyzing complex datasets with many features, SuSiE-PCA helps you identify the most important underlying patterns and the specific features that drive them. You input your high-dimensional data, and it outputs a clearer picture of key factors and the probability that each original feature contributes to those factors. This tool is for researchers and data scientists working with large datasets, especially in fields like genomics or social sciences, who need to pinpoint significant variables.
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Use this if you need to simplify high-dimensional data, identify hidden factors, and understand which specific variables are most important for each factor, especially when you suspect only a few variables truly contribute to each underlying pattern.
Not ideal if your data is not high-dimensional, you don't need to identify sparse relationships, or you prefer traditional, less interpretable dimensionality reduction methods.
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Language
Python
License
MIT
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Last pushed
Feb 13, 2024
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