Summary:
In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs). The essential feature of SOMs, besides dimensional reduction into a discrete map, is the conservation of topology. In SOMs, two forms of learning are applied: competitive, by sequential allocation of sample observations to a winning node in the map, and cooperative, by the update of the weights of the winning node and its neighbors. By means of cooperative learning, the conservation of topology from the original data space to the reduced (typically 2D) map is achieved. Here, we compared the performance of one- and two-layer SOMs in the outlier representation task. The same stratified sampling was applied for both the one-layer and two-layer SOMs; although, stratification would only be relevant for the two-layer setting—to estimate the outlying mixture component detection power. Two distance measures between points in the map were defined to quantify the conservation of topology. The results of the experiment showed that the two-layer setting was more efficient in outlier detection while maintaining the basic properties of the SOM, which included adequately representing distances from the outlier component to the remaining ones.
Keywords: self-organizing map; neural networks; robustness; nonlinear projections; dimensionality reduction; deep semisupervised learning
JCR Impact Factor and WoS quartile: 2,838 - Q2 (2021); 2,700 - Q2 (2022)
DOI reference:
https://doi.org/10.3390/app11146241
Published on paper: July 2021.
Published on-line: July 2021.
Citation:
G. Valverde, J.M. Mira McWilliams, B. González-Pérez, One-layer vs. two-layer som in the context of outlier identification: a simulation study. Applied Sciences. Vol. 11, nº. 14, pp. 6241-1 - 6241-22, July 2021. [Online: July 2021]
Download this paper