The Evolving Tree—A Novel Self-Organizing Network for Data Analysis

The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.

[1]  Andreas Rauber,et al.  Recent Advances with the Growing Hierarchical Self-Organizing Map , 2001, WSOM.

[2]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[3]  Ricardo A. Baeza-Yates,et al.  Searching in metric spaces , 2001, CSUR.

[4]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Erkki Oja,et al.  PicSOM-self-organizing image retrieval with MPEG-7 content descriptors , 2002, IEEE Trans. Neural Networks.

[6]  Risto Miikkulainen,et al.  Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map , 1993, IEEE International Conference on Neural Networks.

[7]  Erkki Oja,et al.  PicSOM - content-based image retrieval with self-organizing maps , 2000, Pattern Recognit. Lett..

[8]  Jussi Pakkanen.  The Evolving Tree: a new kind of self-organizing neural network , 2003 .

[9]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[10]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[11]  Gerald Sommer,et al.  Dynamic Cell Structure Learns Perfectly Topology Preserving Map , 1995, Neural Computation.

[12]  Jukka Iivarinen,et al.  Defect Image Classification and Retrieval with MPEG-7 Descriptors , 2003, SCIA.