Abstract
Sparse models embed variable selection into model learning (e.g., by using l 1-norm regularizer). In small-sample high-dimensional problems, this leads to improved generalization accuracy combined with interpretability, which is important in scientific applications such as biology. In this paper, we summarize our recent work on sparse models, including both sparse regression and sparse Gaussian Markov Random Fields (GMRF), in neuroimaging applications, such as functional MRI data analysis, where the central objective is to gain a better insight into brain functioning, besides just learning predictive models of mental states from imaging data.
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Rish, I. (2013). Functional MRI Analysis with Sparse Models. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_43
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DOI: https://doi.org/10.1007/978-3-642-40994-3_43
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