Name
Abstract | ||
|---|---|---|
This paper studies permutation-based dimension reduction, which can be implemented by first scrambling the input data, then applying the FFT, DCT or Walsh-Hadamard transform and finally using either uniformly random sampling or sparse random projection. By exploiting concentration inequalities of random permutation, we show that this subclass of operators can offer (near) optimal theoretical guarantee. Besides, as random permutation of N elements can be implemented in O(N) time, the proposed algorithm has very low complexity. Some numerical examples are presented to demonstrate the validity of our theoretical development and their promising applications in image processing. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652780 | ICIP |
Keywords | Field | DocType |
stein's method,image processing,walsh functions,random permutation,random processes,fft,discrete cosine transforms,image sampling,compressed sensing,structurally random matrix,hadamard transforms,random sampling,johnson-lindenstrauss lemma,dct,sparse random projection,walsh-hadamard transform,dimension reduction,permutation-based dimension reduction,fast fourier transforms,image retrieval,simulation,sparse matrices,random matrix,stein s method | Random projection,Computer science,Random permutation,Bit-reversal permutation,Artificial intelligence,Random function,Discrete mathematics,Pattern recognition,Random permutation statistics,Permutation matrix,Algorithm,Pseudorandom permutation,Circular shift | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Gan | 1 | 324 | 25.46 |
| Thong T. Do | 2 | 234 | 12.76 |
| Trac D. Tran | 3 | 1507 | 108.22 |