Abstract:BackgroundHigh dimensionality, i.e. p > n, is an inherent feature of machine learning. Fitting a classification model directly to p-dimensional data risks overfitting and a reduction in accuracy. Thus, dimensionality reduction is necessary to address overfitting and high dimensionality.ResultsWe present a novel dimensionality reduction method which uses sparse, orthogonal projections to discover linear separations in reduced dimension space. The technique is applied to miRNA expression analysis and cancer p… Show more
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