A fast learning algorithm for high dimensional problems: an application to microarrays

In this work, a new learning method for one-layer neural network based on a singular value decomposition is presented. The optimal parameters of the model can be obtained by means of a system of linear equations whose complexity depends on the number of samples. This approach provides a fast learning algorithm for huge dimensional problems where the number of inputs is higher than the number of data points. These kinds of situations appear, for example, in DNA microarrays scenarios. An experimental study over eleven microarray datasets shows that the proposed method is able to outperform other representative classifiers, in terms of CPU time, without significant loss of accuracy.