Journee, M and Teschendorff, AE and Absil, PA and Sepulchre, R (2007) Geometric optimization methods for independent component analysis applied on gene expression data. In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol IV, Pts 1-3. (pp. 1413 - 1416). IEEE
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DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identifies a robust contrast function and proposes a new ICA algorithm.
|Title:||Geometric optimization methods for independent component analysis applied on gene expression data|
|Event:||32nd IEEE International Conference on Acoustics, Speech and Signal Processing|
|Dates:||2007-04-15 - 2007-04-20|
|Keywords:||Independent Component Analysis (ICA), optimization on matrix manifolds, RADICAL algorithm, steepest descent on the orthogonal group, gene expression data, CANCER|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Medical Sciences > Wolfson Institute and Cancer Institute Administration > Cancer Institute > Research Department of Cancer Biology|
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