A discrete optimization approach for SVD best truncation choice based on ROC curves

Truncated Singular Value Decomposition (SVD) has always been a key algorithm in modern machine learning. Scientists and researchers use this applied mathematics method in many fields. Despite a long history and prevalence, the issue of how to choose the best truncation level still remains an open challenge. In this paper, we describe a new algorithm, akin a the discrete optimization method, that relies on the Receiver Operating Characteristics (ROC) Areas Under the Curve (AUCs) computation. We explore a concrete application of the algorithm to a bioinformatics problem, i.e. the prediction of biomolecular annotations. We applied the algorithm to nine different datasets and the obtained results demonstrate the effectiveness of our technique.

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