Automatic selection of parameters in LLE
| Autor: | Juliana Valencia Aguirre, Andrés Marino Álvarez Meza, Genaro Daza Santacoloma, Carlos Daniel Acosta Medina, Germán Castellanos Domínguez |
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| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | Revista Facultad de Ingeniería Universidad de Antioquia, Iss 56 (2013) |
| Druh dokumentu: | article |
| ISSN: | 0120-6230 2422-2844 |
| DOI: | 10.17533/udea.redin.14665 |
| Popis: | Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique, which preserves the local geometry of high dimensional space performing an embedding to low dimensional space. LLE algorithm has 3 free parameters that must be set to calculate the embedding: the number of nearest neighbors k, the output space dimensionality m and the regularization parameter a. The last one only is necessary when the value of k is greater than the dimensionality of input space or data are not located in general position, and it plays an important role in the embedding results. In this paper we propose a pair of criteria to find the optimum value for the parameters kand a, to obtain an embedding that faithfully represent the input data space. Our approaches are tested on 2 artificial data sets and 2 real world data sets to verify the effectiveness of the proposed criteria, besides the results are compared against methods found in the state of art. |
| Databáze: | Directory of Open Access Journals |
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