TY - GEN
T1 - Newton-like methods for parallel independent component analysis
AU - Shen, Hao
AU - Hüper, Knut
PY - 2006
Y1 - 2006
N2 - Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear ICA problem from an algorithmic point of view. It is well known that after a pre-whitening process, linear ICA problem can be solved via an optimisation approach on a suitable manifold. FastICA is one prominent linear ICA algorithm for solving the so-called one-unit ICA problem, which was recently shown by the authors to be an approximate Newton's method on the real projective space. To extract multiple components in parallel, in this paper, we propose an approximate Newton-like ICA algorithm on the orthogonal group. The local quadratic convergence properties are discussed. The performance of the proposed algorithms is compared with several existing parallel ICA algorithms by numerical experiments.
AB - Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear ICA problem from an algorithmic point of view. It is well known that after a pre-whitening process, linear ICA problem can be solved via an optimisation approach on a suitable manifold. FastICA is one prominent linear ICA algorithm for solving the so-called one-unit ICA problem, which was recently shown by the authors to be an approximate Newton's method on the real projective space. To extract multiple components in parallel, in this paper, we propose an approximate Newton-like ICA algorithm on the orthogonal group. The local quadratic convergence properties are discussed. The performance of the proposed algorithms is compared with several existing parallel ICA algorithms by numerical experiments.
UR - http://www.scopus.com/inward/record.url?scp=38949140163&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2006.275562
DO - 10.1109/MLSP.2006.275562
M3 - Conference contribution
SN - 1424406560
SN - 9781424406562
T3 - Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
SP - 283
EP - 288
BT - Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
PB - IEEE Computer Society
T2 - 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Y2 - 6 September 2006 through 8 September 2006
ER -