SVM clustering

Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being considered in a number of different ways. Results: An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labelled, this is repeated until an initial convergence occurs. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabelled data) then has its' labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labelled data) and is guaranteed to converge in this situation since it converged previously, and now it has fewer data points to carry with misla...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Stephen Winters-Hilt, Sam Merat
• Keywords: Binary Svm Classifier, BMCBI 2007, SVM-based Clustering, SVM-based Clustering Algorithm