Independent Components Analysis through Product Density Estimation

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Trevor Hastie, Rob Tibshirani


We present a simple direct approach for solving the ICA problem, using density estimation and maximum likelihood. Given a candi(cid:173) date orthogonal frame, we model each of the coordinates using a semi-parametric density estimate based on cubic splines. Since our estimates have two continuous derivatives, we can easily run a sec(cid:173) ond order search for the frame parameters. Our method performs very favorably when compared to state-of-the-art techniques.