Name
Abstract | ||
|---|---|---|
The problem of efficiently compressing a large number, L, of N dimensional signal vectors is considered. The approach suggested here achieves efficiencies over current preprocessing and Karhunen-Loeve techniques when both L and N are large.Preprocessing and partitioning techniques are first ag plied to the L x N data matrix F to reduce the database to a manageable number of subblocks of lower dimension. Within each subblock an iterative chain approximation is proposed that effects a transform at each stage of the iterative scheme. A particularly appealing transform, using prolate spheroidal. sequences, is suggested.To evaluate a reduced dimensionality approximation for the expansion coefficients, the approach used in the orthogonal Procrustes problem solution is combined with an iterative interlacing technique due to Daugavet for factorizing matrices. |
Year | Venue | Keywords |
|---|---|---|
1996 | ISSPA 96 - FOURTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, PROCEEDINGS, VOLS 1 AND 2 | compression algorithms,information processing,matrix decomposition,data compression,signal processing,multidimensional systems,image reconstruction |
Field | DocType | Citations |
Iterative reconstruction,Multidimensional signal processing,Interlacing,Pattern recognition,Computer science,Matrix (mathematics),Matrix decomposition,Orthogonal Procrustes problem,Artificial intelligence,Signal compression,Multidimensional systems | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anatoli Torokhti | 1 | 77 | 12.18 |
| Doug Gray | 2 | 104 | 7.05 |
| S. Elhay | 3 | 28 | 18.57 |
| Phil Howlett | 4 | 128 | 31.75 |
| William Moran | 5 | 1 | 1.87 |