The self-organizing map (SOM) is a traditional neural network algorithm used to achieve feature extraction, clustering, visualization and data exploration. However, it is known that the computational cost of the traditional SOM, used to search for the winner neuron, is expensive especially in case of treating high-dimensional data. In this paper, we propose a novel hierarchical SOM search algorithm which significantly reduces the expensive computational cost associated with traditional SOM. It is shown here that the computational cost of the proposed approach, compared to traditional SOM, to search for the winner neuron is reduced into O(D1 + D2 + ··· + DN) instead of O(D1 × D2 × ··· × DN), where Dj is the number of neurons through a dimension dj of the feature map. At the same time, the new algorithm maintains all merits and qualities of the traditional SOM. Experimental results show that the proposed algorithm is a good alternate to traditional SOM, especially, in high-dimensional feature space problems.
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - Mar 2014|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence