0
$\begingroup$

I am trying to understand this paper called High Dimensionality Reduction Using CUR Matrix Decomposition and Auto-encoder for Web Image Classification. I have understood the method proposed for dimension reduction using CUR decomposition. Only thing i have not understood is this paragraph from point 3.2 :

In our application, as to reduce the dimension of image feature space, we just need to sample matrix $R$ from the column feature space using $ColumnSelect$ Algorithm and project the origin feature vector on the subspace spanned by the columns of $R$, that is $$A_{reduced} = A × R^T$$ where $A_{reduced}$ is the data matrix in reduced space.

Does this paragraph means, that after applying CUR decomposition on matrix $A$, we can directly reduced the dimension using matrix $R$ by projecting matrix $A$ on matrix $R$?

$\endgroup$

Your Answer

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Browse other questions tagged or ask your own question.