A constrained neural learning rule for eliminating the border effect in online self-organising maps
The self-organising map (SOM) is a concise and powerful algorithm for clustering and visualisation of high-dimensional data. However, this robust algorithm still suffers from the border effect. Most of the approaches proposed to eliminate this effect use a borderless topological structure. We prefer to keep the original topological structure of the SOM for visualisation. A novel approach is proposed for the elimination of the border effect from the perspective of self-organising learning. Based on an assumption that the best matching unit (BMU) should be the most active unit, the approach proposes that the BMU should move more towards its associated input sample than its neighbours in the fine-tuned learning stage. Our constrained approach emphasises the effect of the lateral connections and neutralises the effect on the distance between the input sample and units. This approach is able to make units of the map stretch wider than the traditional SOM and thus the border effect is alleviated. Our proposed approach is proved to satisfy the requirements of the topologically ordered neural networks and is evaluated by both qualitative and quantitative criteria. All experiments conclude that performance is improved if the proposed constrained learning rule is used.
neural learning rule border effect self-organising map topological ordered neural network constrained self-organising map