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.
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Grozavu, N., Bennani, Y. (2011). Simultaneous Pattern and Variable Weighting during Topological Clustering. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_68
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DOI: https://doi.org/10.1007/978-3-642-24955-6_68
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