Image compression and data classification by linear programming

This paper explores the possibility of using support vector machines (SVMs) with radial basis function kernels to compress an image such that the parameters of the resulting networks are stored or transmitted. A support vector machine (SVM) has the property that it chooses the minimum number of data points to use as the centres for the Gaussian kernel functions in order to approximate the training data within a given error. A linear programming (LP) based method Is proposed for solving regression and classification problems. Examples of function approximation and class separation illustrate the efficiency of the proposed method. Our results show that Image compression of around 20:1 is achievable while maintaining good image quality.