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evelinag.com | ||
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dm13450.github.io
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| | | | | Principal component analysis (PCA) reduces a dataset to its main components. When we apply it to a dataset of different currencies it helps us understand how each currency drives the overall portfolio and what currency might be a common factor. | |
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sportscidata.com
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| | | | | Recently Dan Weaving and the research group at Leeds Beckett University put out a paper outlining how to perform a type of dimension reduction on training load data: principal component analysis (PCA). The benefit of such an analysis is it can reduce a large number of metrics into a more manageable dataset. This may uncover... | |
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alexhwilliams.info
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| | | | | [AI summary] A technical blog post explaining the mathematical foundations of Principal Component Analysis (PCA), its various generalizations like Sparse and Non-negative Matrix Factorization, and practical considerations for choosing components and handling missing data. | |
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nhigham.com
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| | | In many applications a matrix $latex A\in\mathbb{R}^{m\times n}$ has less than full rank, that is, $latex r = \mathrm{rank}(A) < \min(m,n)$. Sometimes, $latex r$ is known, and a full-rank factorization $LATEX A = GH$ with $latex G\in\mathbb{R}^{m \times r}$ and $latex H\in\mathbb{R}^{r \times n}$, both of rank $latex r$, is given-especially when $latex r =... | ||
