Paper ID sheet
- TITLE: Soft dimension reduction for ICA by joint diagonalization on the Stiefel manifold
- AUTHORS: Fabian J. Theis, Thomas P. Cason, P.-A. Absil
- ABSTRACT:
Joint diagonalization for ICA is often performed on the orthogonal
group after a pre-whitening step. Here we assume that we only want to
extract a few sources after pre-whitening, and hence work on the
Stiefel manifold of $p$-frames in $R^n$. The resulting method does not
only use second-order statistics to estimate the dimension reduction
and is therefore denoted as soft dimension reduction. We employ a
trust-region method for minimizing the cost function on the Stiefel
manifold. Applications to a toy example and functional MRI data show a
higher numerical efficiency, especially when $p$ is much smaller than
$n$, and more robust performance in the presence of strong noise than
methods based on pre-whitening.
- KEY WORDS: independent component analysis (ICA), blind source separation (BSS), joint diagonalization, soft dimension reduction
- STATUS: Lecture Notes in Computer Science, 5441, pp. 354-361, 2009.
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