Abstract: Structurally Random Matrices (SRM) are first proposed in [1] as fast and highly efficient measurement operators for large scale compressed sensing ...
ABSTRACT. Structurally Random Matrices (SRM) are first proposed in [1] as fast and highly efficient measurement operators for large scale com-.
Fast and efficient dimensionality reduction using Structurally Random Matrices · Thong T. Do, Lu Gan, +2 authors. T. Tran · Published in IEEE International ...
Structurally random matrices (SRM) are first proposed in as fast and highly efficient measurement operators for large scale compressed sensing applications.
Structurally Random Matrices (SRM) are first proposed in [1] as fast and highly efficient measurement operators for large scale compressed sensing applications.
Structurally random matrices (SRM) are first proposed in as fast and highly efficient measurement operators for large scale compressed sensing applications.
Aug 29, 2018 · Quick Facts: UMAP can handle large datasets and is faster than t-SNE and also supports fitting to sparse matrix data, and contrary to t-SNE, a ...
Missing: Structurally | Show results with:Structurally
Duration: 53:31
Posted: Aug 16, 2016
Posted: Aug 16, 2016
Missing: Structurally | Show results with:Structurally
ABSTRACT. This paper studies permutation-based dimension reduction, which can be implemented by first scrambling the input data, then applying.
Fast and efficient dimensionality reduction using Structurally Random Matrices. 2009 IEEE International Conference on Acoustics, Speech and Signal Processing ...