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What is Nonnegative PCA and SVD Techniques about?
The paper discusses modifications to Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) that ensure nonnegative loadings for variable aggregation. It introduces exponential, logit, and multinomial parameterizations to achieve this, allowing for clearer interpretation of variable contributions in data approximation. The study also compares classical PCA/SVD results with the new methods using numerical examples and highlights the relevance of Perron-Frobenius theory in interpreting the
- Author
- Nashenas Nashenas
- Language
- EN