Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions


Robert Durrant, Ata Kaban ;
Proceedings of the 5th Asian Conference on Machine Learning, PMLR 29:17-32, 2013.


We examine the performance of an ensemble of randomly-projected Fisher Linear Discriminant classifiers, focusing on the case when there are fewer training observations than data dimensions. Our ensemble is learned from a sequence of randomly-projected representations of the original high dimensional data and therefore for this approach data can be collected, stored and processed in such a compressed form. The specific form and simplicity of this ensemble permits a direct and much more detailed analysis than existing generic tools in previous works. In particular, we are able to derive the exact form of the generalization error of our ensemble, conditional on the training set, and based on this we give theoretical guarantees which directly link the performance of the ensemble to that of the corresponding linear discriminant learned in the full data space. To the best of our knowledge these are the first theoretical results to prove such an explicit link for any classifier and classifier ensemble pair. Furthermore we show that the randomly-projected ensemble is equivalent to implementing a sophisticated regularization scheme to the linear discriminant learned in the original data space and this prevents overfitting in conditions of small sample size where pseudo-inverse FLD learned in the data space is provably poor.

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