Name
Abstract | ||
|---|---|---|
This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse applications, including image processing and dimensionality reduction of time series in wireless sensor networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MLSP.2014.6958849 | Machine Learning for Signal Processing |
Keywords | Field | DocType |
covariance matrices,principal component analysis,unsupervised learning,cooperative diffusion-based strategy,covariance matrix,in-network principal component analysis,Principal component analysis,adaptive learning,distributed processing,network | Multiple correspondence analysis,Sparse PCA,Dimensionality reduction,Pattern recognition,Computer science,Image processing,Principal axis theorem,Kernel principal component analysis,Artificial intelligence,Wireless sensor network,Machine learning,Principal component analysis | Conference |
ISSN | Citations | PageRank |
2161-0363 | 3 | 0.41 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ghadban, N. | 1 | 3 | 0.41 |
| Honeine, P. | 2 | 11 | 1.92 |
| Mourad-Chehade, F. | 3 | 4 | 1.10 |
| Clovis Francis | 4 | 34 | 11.20 |