Name
Abstract | ||
|---|---|---|
We present a new neural model, which extends Kohonen's self-organizing map (SOM) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. Several self-organizing maps have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model at each neuron while it has linear complexity on the dimensionality of the input space. This allows to process very high dimensional data to obtain reliable estimations of the local probability densities which are based on the PPCA framework. Experimental results are presented, which show the map formation capabilities of the proposal with high dimensional data. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73007-1_26 | IWANN |
Keywords | Field | DocType |
ppca framework,probabilistic pca model,probabilistic model,new neural model,self-organizing map,local probability density,probabilistic principal,map formation capability,local principal subspaces,high dimensional data,components analysis,competitive learning,self organization,dimensionality reduction,probability density,unsupervised learning,face recognition | Competitive learning,Clustering high-dimensional data,Dimensionality reduction,Pattern recognition,Computer science,Self-organizing map,Probabilistic analysis of algorithms,Statistical model,Artificial intelligence,Probabilistic logic,Machine learning,Principal component analysis | Conference |
Volume | ISSN | Citations |
4507 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ezequiel López-Rubio | 1 | 323 | 39.73 |
| Juan Miguel Ortiz-de-lazcano-lobato | 2 | 68 | 11.59 |
| Domingo López-Rodríguez | 3 | 55 | 9.24 |
| María Del Carmen Vargas-gonzález | 4 | 8 | 2.90 |