Name
Abstract | ||
|---|---|---|
This paper addresses the problem of detecting a subset of the most relevant features and observations from a dataset through a local weighted learning paradigm. We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and double local weighting. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector) and an observation weighting matrix. Based on the lwo-SOM and lwd-SOM [7], we present a new weighting approach allowing to take into account the importance of the observations and of the variables simultaneously called dlw-SOM. After the learning phase, a selection method is used with weight vectors to prune the irrelevant variables and thus we can characterize the clusters. A number of synthetic and real data are experimented on to show the benefits of the proposed double local weighting using self-organizing models. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24955-6_68 | ICONIP (1) |
Keywords | Field | DocType |
new learning approach,proposed double local weighting,different features vector weight,observation weighting matrix,simultaneous pattern,relevance vector,proposed approach,local weighting,topological clustering,weight vector,local weighted learning paradigm,new weighting approach,variable weighting | Cluster (physics),Data mining,Weighting,Pattern recognition,Feature selection,Computer science,Matrix (mathematics),Self-organizing map,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
7062 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nistor Grozavu | 1 | 67 | 16.76 |
| Younès Bennani | 2 | 269 | 53.18 |