Sparse Greedy Matrix Approximation for Machine Learning

In kernel based methods such as Regularization Networks large datasets pose significant problems since the number of basis functions required for an optimal solution equals the number of samples. We present a sparse greedy approximation technique to construct a compressed representation of the design matrix. Experimental results are given and connections to Kernel-PCA, Sparse Kernel Feature Analysis, and Matching Pursuit are pointed out.