GNG-SVM framework - classifying large datasets with Support Vector Machines using Growing Neural Gas

Support vector machines (SVMs) represent a well known technique for data classification. However, the complexity of the training process makes the SVMs unsuitable for classifying large datasets. Examples of existing approaches to this problem are sampling of the input datasets or clustering of similar inputs. On the other hand, the growing neural gas algorithm (GNG) is a robust tool for cluster analysis, capable of learning the topology of the data. It overcomes most of the common issues of clustering techniques such as predefined number of clusters or beforehand specified cluster radius. This paper presents a solution to the problem of classifying large datasets via learning of the data topology. The described algorithm combines the GNG algorithm with the SVM solver into a specific algorithm for classification of large datasets - the GNG-SVM framework. The input dataset is first preprocessed with the GNG algorithm. A new reduced training dataset is created from the extracted topological knowledge. Because the size of the dataset is significantly reduced, the training process of the SVM solver becomes substantially less memory demanding. The performance of the proposed GNG-SVM framework is tested on both synthetic and benchmark real world datasets.