Name
Abstract | ||
|---|---|---|
Nonlinear principal component analysis is one of the best dimension reduction techniques developed during the recent years which have been applied in different signal-processing applications. In this paper, an evolving category of auto-associative neural network is presented which is applied to perform dynamic nonlinear principal component analysis. Training strategy of the network implements both constructive and destructive algorithms to extract dynamic principal components of speech database. In addition, the proposed network makes it possible to eliminate some dimensions of sequences that do not play important role in the quality of speech processing. Finally, the network is successfully applied to solve missing data problem. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s00521-009-0328-1 | Neural Computing and Applications |
Keywords | DocType | Volume |
proposed network,evolving auto-associative neural network � dynamic principal component analysis � missing data problemspeech compression,auto-associative neural network,dynamic principal component analysis,destructive algorithm,best dimension reduction technique,dynamic principal component,dynamic nonlinear principal component,different signal-processing application,speech database,Nonlinear principal component analysis,speech processing | Journal | 19 |
Issue | ISSN | Citations |
3 | 1433-3058 | 6 |
PageRank | References | Authors |
0.55 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Behrooz Makki | 1 | 366 | 34.36 |
| Mona Noori Hosseini | 2 | 28 | 4.85 |
| Seyyed Ali Seyyedsalehi | 3 | 81 | 8.11 |