A sparse least squares support vector machine classifier

Since the early 90's, Support Vector Machines (SVM) are attracting more and more attention due to their applicability to a large number of problems. To overcome the high computational complexity of traditional Support Vector Machines, recently a new technique, the Least-Squares SVM (LS-SVM) has been introduced, but unfortunately a very attractive feature of SVM, namely its sparseness, was lost. LS-SVM simplifies the required computation to solving linear equation set. This equation set embodies all available information about the learning process. By applying modifications to this equation set, we present a Least Squares version of the Least Squares Support Vector Machine (LS2-SVM). The proposed modification speeds up the calculations and provides better results, but most importantly it concludes a sparse solution.