Journée, M and Teschendorff, AE and Absil, P-A and Sepulchre, R (2007) Geometric optimization methods for independent component analysis applied on gene expression data. In: UNSPECIFIED.Full text not available from this repository.
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. © 2007 IEEE.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Uncontrolled Keywords:||Gene expression data Independent Component Analysis (ICA) Optimization on matrix manifolds RADICAL algorithm Steepest-descent on the orthogonal group|
|Divisions:||Div F > Control|
|Depositing User:||Cron job|
|Date Deposited:||04 Feb 2015 22:50|
|Last Modified:||05 Feb 2015 08:22|