التفاصيل البيبلوغرافية
| العنوان: |
P1DSOM - A FAST SEARCH ALGORITHM FOR HIGH-DIMENSIONAL FEATURE SPACE PROBLEMS. |
| المؤلفون: |
SAGHEER, ALAA, TSURUTA, NAYOUKI, TANIGUCHI, RIN-ICHIRO |
| المصدر: |
International Journal of Pattern Recognition & Artificial Intelligence; Mar2014, Vol. 28 Issue 2, p-1, 19p |
| مصطلحات موضوعية: |
SEARCH algorithms, FEATURE extraction, PROBLEM solving, SELF-organizing maps, HUMAN facial recognition software, COMPUTATIONAL complexity |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
