4 ms·

QUOTE: << Is it possible to condense high-dimensional data into smaller dimensions and retain the important geometric properties of the data? >> Like in a non

• zardo 11y ago
  No, the meaning dimension there is different. In the Reynolds number it's a ratio of measures where the units cancel. Here we're talking about mapping data into a high dimensional space, then trying to project that down to the minimum dimension space that can preserve the relevant information. In the example we don't have any reason to even think all our dimensions are orthogonal, we're assigning a new dimension for each word, but we know there is a lot of overlap in word meaning.
  • ergl 11y ago
    > Here we're talking about mapping data into a high dimensional space, then trying to project that down to the minimum dimension space that can preserve the relevant information. Isn't this what M/PCA is all about? https://en.wikipedia.org/wiki/Principal_component_analysis https://en.wikipedia.org/wiki/Principal_component_analysis and https://en.wikipedia.org/wiki/Multilinear_principal_component_analysis https://en.wikipedia.org/wiki/Multilinear_principal_componen...