Fast Krylov Methods for N-Body Learning

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Nando Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang


This paper addresses the issue of numerical computation in machine learning domains based on similarity metrics, such as kernel methods, spectral techniques and Gaussian processes. It presents a general solution strategy based on Krylov subspace iteration and fast N-body learning methods. The experiments show significant gains in computation and storage on datasets arising in image segmentation, object detection and dimensionality reduction. The paper also presents theoretical bounds on the stability of these methods.