Name
Abstract | ||
|---|---|---|
In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low complexity on the dimensionality of the input space. This allows to process very high-dimensional data to obtain reliable estimations of the 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, and its potential in image and video compression applications. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TNN.2009.2025888 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
ppca framework,high-dimensional data,self-organizing map,probabilistic principal component analysis,probabilistic pca self-organizing map,map formation capability,input space,probabilistic neural model,local principal subspaces,probabilistic model,vectors,video compression,computer vision,handwriting recognition,unsupervised learning,covariance matrix,computational complexity,dimensionality reduction,probability density,image compression,competitive learning,high dimensional data,principal component analysis,probability | Competitive learning,Dimensionality reduction,Pattern recognition,Computer science,Self-organizing map,Statistical model,Artificial intelligence,Probabilistic logic,Data compression,Artificial neural network,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 9 | 1941-0093 |
Citations | PageRank | References |
23 | 0.80 | 53 |
Authors | ||
3 | ||
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 |