Trading Approximation Quality Versus Sparsity Within Incremental Automatic Relevance Determination Frameworks

Provided by: Graz University of Technology
Topic: Mobility
Format: PDF
In this paper a trade-off between sparsity and approximation quality of models learned with Incremental Automatic Relevance Determination (IARD) is addressed. An IARD algorithm is a class of Sparse Bayesian Learning (SBL) schemes. It permits an intuitive and simple adjustment of estimation expressions, with the adjustment having a simple interpretation in terms of Signal-to-Noise Ratio (SNR). This adjustment allows for implementing a trade-off between sparsity of the estimated model versus its accuracy in terms of residual Mean-Square Error (MSE). It is found that this adjustment has a different impact on the IARD performance, depending on whether the measurement model coincides with the used estimation model or not.

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