Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : Proceedings - 2015 5th International Conference on Advances in Computing and Communications, ICACC 2015, Institute of Electrical and Electronics Engineers .
Source : Proceedings - 2015 5th International Conference on Advances in Computing and Communications, ICACC 2015, Institute of Electrical and Electronics Engineers Inc., p.434-438 (2015)
ISBN : 9781467369947
Keywords : Comparative performance analysis, Conformal mapping, Dimensionality reduction, High dimensional datasets, High-dimensional, NLPCA, Real-world datasets, Self organizing maps, Time consumption, Unsupervised learning, Weight initialization
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : AmritaCREATE
Department : Computer Science
Year : 2015
Abstract : Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses on the analysis of different weight initialization strategies and various dimensionality reduction measures with the intent to make SOM flexible for handling high-dimensional datasets. We use two methods of comparison, one on projected space and another before projection. The datasets used are real world datasets taken from UCI repository. © 2015 IEEE.
Cite this Research Publication : Ha Haripriya, DeviSree, Rb, Pooja, Db, and Prof. Prema Nedungadi, “A Comparative Performance Analysis of Self Organizing Maps on Weight Initializations Using different Strategies”, in Proceedings - 2015 5th International Conference on Advances in Computing and Communications, ICACC 2015, 2015, pp. 434-438
